DATA 624 - Homework4 - Data Pre-Processing & Overfitting

Libraries

library(kableExtra)
library(tidyverse)
library(ggplot2)
library(dplyr)
library(TSstudio)
library(RColorBrewer)
library(GGally)
library(grid)
library(gridExtra)
library(mlbench)
library(psych)
library(cowplot)
library(corrplot)
library(caret)
library(geoR)
library(reshape)
library(naniar)
library(mice)
library(DMwR)
#library(missForest)

Applied Predictive Modeling

Exercise 3.1

The UC Irvine Machine Learning Repository6 contains a data set related to glass identification. The data consist of 214 glass samples labeled as one of seven class categories. There are nine predictors, including the refractive index and percentages of eight elements: Na, Mg, Al, Si, K, Ca, Ba, and Fe.

Data Description:

A data frame with 214 observation containing examples of the chemical analysis of 7 different types of glass. This data set can be leveraged to forecast the type of class on basis of the chemical analysis. The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence (if it is correctly identified!).

Data Format: A data frame with 214 observations on 10 variables:

data(Glass)
str(Glass)

## 'data.frame':    214 obs. of  10 variables:
##  $ RI  : num  1.52 1.52 1.52 1.52 1.52 ...
##  $ Na  : num  13.6 13.9 13.5 13.2 13.3 ...
##  $ Mg  : num  4.49 3.6 3.55 3.69 3.62 3.61 3.6 3.61 3.58 3.6 ...
##  $ Al  : num  1.1 1.36 1.54 1.29 1.24 1.62 1.14 1.05 1.37 1.36 ...
##  $ Si  : num  71.8 72.7 73 72.6 73.1 ...
##  $ K   : num  0.06 0.48 0.39 0.57 0.55 0.64 0.58 0.57 0.56 0.57 ...
##  $ Ca  : num  8.75 7.83 7.78 8.22 8.07 8.07 8.17 8.24 8.3 8.4 ...
##  $ Ba  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Fe  : num  0 0 0 0 0 0.26 0 0 0 0.11 ...
##  $ Type: Factor w/ 6 levels "1","2","3","5",..: 1 1 1 1 1 1 1 1 1 1 ...

**Preview of the Data:**

Glass %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

## Warning: namespace 'highr' is not available and has been replaced
## by .GlobalEnv when processing object '<unknown>'

RI	Na	Mg	Al	Si	K	Ca	Ba	Fe	Type
1.52101	13.64	4.49	1.10	71.78	0.06	8.75	0.00	0.00	1
1.51761	13.89	3.60	1.36	72.73	0.48	7.83	0.00	0.00	1
1.51618	13.53	3.55	1.54	72.99	0.39	7.78	0.00	0.00	1
1.51766	13.21	3.69	1.29	72.61	0.57	8.22	0.00	0.00	1
1.51742	13.27	3.62	1.24	73.08	0.55	8.07	0.00	0.00	1
1.51596	12.79	3.61	1.62	72.97	0.64	8.07	0.00	0.26	1
1.51743	13.30	3.60	1.14	73.09	0.58	8.17	0.00	0.00	1
1.51756	13.15	3.61	1.05	73.24	0.57	8.24	0.00	0.00	1
1.51918	14.04	3.58	1.37	72.08	0.56	8.30	0.00	0.00	1
1.51755	13.00	3.60	1.36	72.99	0.57	8.40	0.00	0.11	1
1.51571	12.72	3.46	1.56	73.20	0.67	8.09	0.00	0.24	1
1.51763	12.80	3.66	1.27	73.01	0.60	8.56	0.00	0.00	1
1.51589	12.88	3.43	1.40	73.28	0.69	8.05	0.00	0.24	1
1.51748	12.86	3.56	1.27	73.21	0.54	8.38	0.00	0.17	1
1.51763	12.61	3.59	1.31	73.29	0.58	8.50	0.00	0.00	1
1.51761	12.81	3.54	1.23	73.24	0.58	8.39	0.00	0.00	1
1.51784	12.68	3.67	1.16	73.11	0.61	8.70	0.00	0.00	1
1.52196	14.36	3.85	0.89	71.36	0.15	9.15	0.00	0.00	1
1.51911	13.90	3.73	1.18	72.12	0.06	8.89	0.00	0.00	1
1.51735	13.02	3.54	1.69	72.73	0.54	8.44	0.00	0.07	1
1.51750	12.82	3.55	1.49	72.75	0.54	8.52	0.00	0.19	1
1.51966	14.77	3.75	0.29	72.02	0.03	9.00	0.00	0.00	1
1.51736	12.78	3.62	1.29	72.79	0.59	8.70	0.00	0.00	1
1.51751	12.81	3.57	1.35	73.02	0.62	8.59	0.00	0.00	1
1.51720	13.38	3.50	1.15	72.85	0.50	8.43	0.00	0.00	1
1.51764	12.98	3.54	1.21	73.00	0.65	8.53	0.00	0.00	1
1.51793	13.21	3.48	1.41	72.64	0.59	8.43	0.00	0.00	1
1.51721	12.87	3.48	1.33	73.04	0.56	8.43	0.00	0.00	1
1.51768	12.56	3.52	1.43	73.15	0.57	8.54	0.00	0.00	1
1.51784	13.08	3.49	1.28	72.86	0.60	8.49	0.00	0.00	1
1.51768	12.65	3.56	1.30	73.08	0.61	8.69	0.00	0.14	1
1.51747	12.84	3.50	1.14	73.27	0.56	8.55	0.00	0.00	1
1.51775	12.85	3.48	1.23	72.97	0.61	8.56	0.09	0.22	1
1.51753	12.57	3.47	1.38	73.39	0.60	8.55	0.00	0.06	1
1.51783	12.69	3.54	1.34	72.95	0.57	8.75	0.00	0.00	1
1.51567	13.29	3.45	1.21	72.74	0.56	8.57	0.00	0.00	1
1.51909	13.89	3.53	1.32	71.81	0.51	8.78	0.11	0.00	1
1.51797	12.74	3.48	1.35	72.96	0.64	8.68	0.00	0.00	1
1.52213	14.21	3.82	0.47	71.77	0.11	9.57	0.00	0.00	1
1.52213	14.21	3.82	0.47	71.77	0.11	9.57	0.00	0.00	1
1.51793	12.79	3.50	1.12	73.03	0.64	8.77	0.00	0.00	1
1.51755	12.71	3.42	1.20	73.20	0.59	8.64	0.00	0.00	1
1.51779	13.21	3.39	1.33	72.76	0.59	8.59	0.00	0.00	1
1.52210	13.73	3.84	0.72	71.76	0.17	9.74	0.00	0.00	1
1.51786	12.73	3.43	1.19	72.95	0.62	8.76	0.00	0.30	1
1.51900	13.49	3.48	1.35	71.95	0.55	9.00	0.00	0.00	1
1.51869	13.19	3.37	1.18	72.72	0.57	8.83	0.00	0.16	1
1.52667	13.99	3.70	0.71	71.57	0.02	9.82	0.00	0.10	1
1.52223	13.21	3.77	0.79	71.99	0.13	10.02	0.00	0.00	1
1.51898	13.58	3.35	1.23	72.08	0.59	8.91	0.00	0.00	1
1.52320	13.72	3.72	0.51	71.75	0.09	10.06	0.00	0.16	1
1.51926	13.20	3.33	1.28	72.36	0.60	9.14	0.00	0.11	1
1.51808	13.43	2.87	1.19	72.84	0.55	9.03	0.00	0.00	1
1.51837	13.14	2.84	1.28	72.85	0.55	9.07	0.00	0.00	1
1.51778	13.21	2.81	1.29	72.98	0.51	9.02	0.00	0.09	1
1.51769	12.45	2.71	1.29	73.70	0.56	9.06	0.00	0.24	1
1.51215	12.99	3.47	1.12	72.98	0.62	8.35	0.00	0.31	1
1.51824	12.87	3.48	1.29	72.95	0.60	8.43	0.00	0.00	1
1.51754	13.48	3.74	1.17	72.99	0.59	8.03	0.00	0.00	1
1.51754	13.39	3.66	1.19	72.79	0.57	8.27	0.00	0.11	1
1.51905	13.60	3.62	1.11	72.64	0.14	8.76	0.00	0.00	1
1.51977	13.81	3.58	1.32	71.72	0.12	8.67	0.69	0.00	1
1.52172	13.51	3.86	0.88	71.79	0.23	9.54	0.00	0.11	1
1.52227	14.17	3.81	0.78	71.35	0.00	9.69	0.00	0.00	1
1.52172	13.48	3.74	0.90	72.01	0.18	9.61	0.00	0.07	1
1.52099	13.69	3.59	1.12	71.96	0.09	9.40	0.00	0.00	1
1.52152	13.05	3.65	0.87	72.22	0.19	9.85	0.00	0.17	1
1.52152	13.05	3.65	0.87	72.32	0.19	9.85	0.00	0.17	1
1.52152	13.12	3.58	0.90	72.20	0.23	9.82	0.00	0.16	1
1.52300	13.31	3.58	0.82	71.99	0.12	10.17	0.00	0.03	1
1.51574	14.86	3.67	1.74	71.87	0.16	7.36	0.00	0.12	2
1.51848	13.64	3.87	1.27	71.96	0.54	8.32	0.00	0.32	2
1.51593	13.09	3.59	1.52	73.10	0.67	7.83	0.00	0.00	2
1.51631	13.34	3.57	1.57	72.87	0.61	7.89	0.00	0.00	2
1.51596	13.02	3.56	1.54	73.11	0.72	7.90	0.00	0.00	2
1.51590	13.02	3.58	1.51	73.12	0.69	7.96	0.00	0.00	2
1.51645	13.44	3.61	1.54	72.39	0.66	8.03	0.00	0.00	2
1.51627	13.00	3.58	1.54	72.83	0.61	8.04	0.00	0.00	2
1.51613	13.92	3.52	1.25	72.88	0.37	7.94	0.00	0.14	2
1.51590	12.82	3.52	1.90	72.86	0.69	7.97	0.00	0.00	2
1.51592	12.86	3.52	2.12	72.66	0.69	7.97	0.00	0.00	2
1.51593	13.25	3.45	1.43	73.17	0.61	7.86	0.00	0.00	2
1.51646	13.41	3.55	1.25	72.81	0.68	8.10	0.00	0.00	2
1.51594	13.09	3.52	1.55	72.87	0.68	8.05	0.00	0.09	2
1.51409	14.25	3.09	2.08	72.28	1.10	7.08	0.00	0.00	2
1.51625	13.36	3.58	1.49	72.72	0.45	8.21	0.00	0.00	2
1.51569	13.24	3.49	1.47	73.25	0.38	8.03	0.00	0.00	2
1.51645	13.40	3.49	1.52	72.65	0.67	8.08	0.00	0.10	2
1.51618	13.01	3.50	1.48	72.89	0.60	8.12	0.00	0.00	2
1.51640	12.55	3.48	1.87	73.23	0.63	8.08	0.00	0.09	2
1.51841	12.93	3.74	1.11	72.28	0.64	8.96	0.00	0.22	2
1.51605	12.90	3.44	1.45	73.06	0.44	8.27	0.00	0.00	2
1.51588	13.12	3.41	1.58	73.26	0.07	8.39	0.00	0.19	2
1.51590	13.24	3.34	1.47	73.10	0.39	8.22	0.00	0.00	2
1.51629	12.71	3.33	1.49	73.28	0.67	8.24	0.00	0.00	2
1.51860	13.36	3.43	1.43	72.26	0.51	8.60	0.00	0.00	2
1.51841	13.02	3.62	1.06	72.34	0.64	9.13	0.00	0.15	2
1.51743	12.20	3.25	1.16	73.55	0.62	8.90	0.00	0.24	2
1.51689	12.67	2.88	1.71	73.21	0.73	8.54	0.00	0.00	2
1.51811	12.96	2.96	1.43	72.92	0.60	8.79	0.14	0.00	2
1.51655	12.75	2.85	1.44	73.27	0.57	8.79	0.11	0.22	2
1.51730	12.35	2.72	1.63	72.87	0.70	9.23	0.00	0.00	2
1.51820	12.62	2.76	0.83	73.81	0.35	9.42	0.00	0.20	2
1.52725	13.80	3.15	0.66	70.57	0.08	11.64	0.00	0.00	2
1.52410	13.83	2.90	1.17	71.15	0.08	10.79	0.00	0.00	2
1.52475	11.45	0.00	1.88	72.19	0.81	13.24	0.00	0.34	2
1.53125	10.73	0.00	2.10	69.81	0.58	13.30	3.15	0.28	2
1.53393	12.30	0.00	1.00	70.16	0.12	16.19	0.00	0.24	2
1.52222	14.43	0.00	1.00	72.67	0.10	11.52	0.00	0.08	2
1.51818	13.72	0.00	0.56	74.45	0.00	10.99	0.00	0.00	2
1.52664	11.23	0.00	0.77	73.21	0.00	14.68	0.00	0.00	2
1.52739	11.02	0.00	0.75	73.08	0.00	14.96	0.00	0.00	2
1.52777	12.64	0.00	0.67	72.02	0.06	14.40	0.00	0.00	2
1.51892	13.46	3.83	1.26	72.55	0.57	8.21	0.00	0.14	2
1.51847	13.10	3.97	1.19	72.44	0.60	8.43	0.00	0.00	2
1.51846	13.41	3.89	1.33	72.38	0.51	8.28	0.00	0.00	2
1.51829	13.24	3.90	1.41	72.33	0.55	8.31	0.00	0.10	2
1.51708	13.72	3.68	1.81	72.06	0.64	7.88	0.00	0.00	2
1.51673	13.30	3.64	1.53	72.53	0.65	8.03	0.00	0.29	2
1.51652	13.56	3.57	1.47	72.45	0.64	7.96	0.00	0.00	2
1.51844	13.25	3.76	1.32	72.40	0.58	8.42	0.00	0.00	2
1.51663	12.93	3.54	1.62	72.96	0.64	8.03	0.00	0.21	2
1.51687	13.23	3.54	1.48	72.84	0.56	8.10	0.00	0.00	2
1.51707	13.48	3.48	1.71	72.52	0.62	7.99	0.00	0.00	2
1.52177	13.20	3.68	1.15	72.75	0.54	8.52	0.00	0.00	2
1.51872	12.93	3.66	1.56	72.51	0.58	8.55	0.00	0.12	2
1.51667	12.94	3.61	1.26	72.75	0.56	8.60	0.00	0.00	2
1.52081	13.78	2.28	1.43	71.99	0.49	9.85	0.00	0.17	2
1.52068	13.55	2.09	1.67	72.18	0.53	9.57	0.27	0.17	2
1.52020	13.98	1.35	1.63	71.76	0.39	10.56	0.00	0.18	2
1.52177	13.75	1.01	1.36	72.19	0.33	11.14	0.00	0.00	2
1.52614	13.70	0.00	1.36	71.24	0.19	13.44	0.00	0.10	2
1.51813	13.43	3.98	1.18	72.49	0.58	8.15	0.00	0.00	2
1.51800	13.71	3.93	1.54	71.81	0.54	8.21	0.00	0.15	2
1.51811	13.33	3.85	1.25	72.78	0.52	8.12	0.00	0.00	2
1.51789	13.19	3.90	1.30	72.33	0.55	8.44	0.00	0.28	2
1.51806	13.00	3.80	1.08	73.07	0.56	8.38	0.00	0.12	2
1.51711	12.89	3.62	1.57	72.96	0.61	8.11	0.00	0.00	2
1.51674	12.79	3.52	1.54	73.36	0.66	7.90	0.00	0.00	2
1.51674	12.87	3.56	1.64	73.14	0.65	7.99	0.00	0.00	2
1.51690	13.33	3.54	1.61	72.54	0.68	8.11	0.00	0.00	2
1.51851	13.20	3.63	1.07	72.83	0.57	8.41	0.09	0.17	2
1.51662	12.85	3.51	1.44	73.01	0.68	8.23	0.06	0.25	2
1.51709	13.00	3.47	1.79	72.72	0.66	8.18	0.00	0.00	2
1.51660	12.99	3.18	1.23	72.97	0.58	8.81	0.00	0.24	2
1.51839	12.85	3.67	1.24	72.57	0.62	8.68	0.00	0.35	2
1.51769	13.65	3.66	1.11	72.77	0.11	8.60	0.00	0.00	3
1.51610	13.33	3.53	1.34	72.67	0.56	8.33	0.00	0.00	3
1.51670	13.24	3.57	1.38	72.70	0.56	8.44	0.00	0.10	3
1.51643	12.16	3.52	1.35	72.89	0.57	8.53	0.00	0.00	3
1.51665	13.14	3.45	1.76	72.48	0.60	8.38	0.00	0.17	3
1.52127	14.32	3.90	0.83	71.50	0.00	9.49	0.00	0.00	3
1.51779	13.64	3.65	0.65	73.00	0.06	8.93	0.00	0.00	3
1.51610	13.42	3.40	1.22	72.69	0.59	8.32	0.00	0.00	3
1.51694	12.86	3.58	1.31	72.61	0.61	8.79	0.00	0.00	3
1.51646	13.04	3.40	1.26	73.01	0.52	8.58	0.00	0.00	3
1.51655	13.41	3.39	1.28	72.64	0.52	8.65	0.00	0.00	3
1.52121	14.03	3.76	0.58	71.79	0.11	9.65	0.00	0.00	3
1.51776	13.53	3.41	1.52	72.04	0.58	8.79	0.00	0.00	3
1.51796	13.50	3.36	1.63	71.94	0.57	8.81	0.00	0.09	3
1.51832	13.33	3.34	1.54	72.14	0.56	8.99	0.00	0.00	3
1.51934	13.64	3.54	0.75	72.65	0.16	8.89	0.15	0.24	3
1.52211	14.19	3.78	0.91	71.36	0.23	9.14	0.00	0.37	3
1.51514	14.01	2.68	3.50	69.89	1.68	5.87	2.20	0.00	5
1.51915	12.73	1.85	1.86	72.69	0.60	10.09	0.00	0.00	5
1.52171	11.56	1.88	1.56	72.86	0.47	11.41	0.00	0.00	5
1.52151	11.03	1.71	1.56	73.44	0.58	11.62	0.00	0.00	5
1.51969	12.64	0.00	1.65	73.75	0.38	11.53	0.00	0.00	5
1.51666	12.86	0.00	1.83	73.88	0.97	10.17	0.00	0.00	5
1.51994	13.27	0.00	1.76	73.03	0.47	11.32	0.00	0.00	5
1.52369	13.44	0.00	1.58	72.22	0.32	12.24	0.00	0.00	5
1.51316	13.02	0.00	3.04	70.48	6.21	6.96	0.00	0.00	5
1.51321	13.00	0.00	3.02	70.70	6.21	6.93	0.00	0.00	5
1.52043	13.38	0.00	1.40	72.25	0.33	12.50	0.00	0.00	5
1.52058	12.85	1.61	2.17	72.18	0.76	9.70	0.24	0.51	5
1.52119	12.97	0.33	1.51	73.39	0.13	11.27	0.00	0.28	5
1.51905	14.00	2.39	1.56	72.37	0.00	9.57	0.00	0.00	6
1.51937	13.79	2.41	1.19	72.76	0.00	9.77	0.00	0.00	6
1.51829	14.46	2.24	1.62	72.38	0.00	9.26	0.00	0.00	6
1.51852	14.09	2.19	1.66	72.67	0.00	9.32	0.00	0.00	6
1.51299	14.40	1.74	1.54	74.55	0.00	7.59	0.00	0.00	6
1.51888	14.99	0.78	1.74	72.50	0.00	9.95	0.00	0.00	6
1.51916	14.15	0.00	2.09	72.74	0.00	10.88	0.00	0.00	6
1.51969	14.56	0.00	0.56	73.48	0.00	11.22	0.00	0.00	6
1.51115	17.38	0.00	0.34	75.41	0.00	6.65	0.00	0.00	6
1.51131	13.69	3.20	1.81	72.81	1.76	5.43	1.19	0.00	7
1.51838	14.32	3.26	2.22	71.25	1.46	5.79	1.63	0.00	7
1.52315	13.44	3.34	1.23	72.38	0.60	8.83	0.00	0.00	7
1.52247	14.86	2.20	2.06	70.26	0.76	9.76	0.00	0.00	7
1.52365	15.79	1.83	1.31	70.43	0.31	8.61	1.68	0.00	7
1.51613	13.88	1.78	1.79	73.10	0.00	8.67	0.76	0.00	7
1.51602	14.85	0.00	2.38	73.28	0.00	8.76	0.64	0.09	7
1.51623	14.20	0.00	2.79	73.46	0.04	9.04	0.40	0.09	7
1.51719	14.75	0.00	2.00	73.02	0.00	8.53	1.59	0.08	7
1.51683	14.56	0.00	1.98	73.29	0.00	8.52	1.57	0.07	7
1.51545	14.14	0.00	2.68	73.39	0.08	9.07	0.61	0.05	7
1.51556	13.87	0.00	2.54	73.23	0.14	9.41	0.81	0.01	7
1.51727	14.70	0.00	2.34	73.28	0.00	8.95	0.66	0.00	7
1.51531	14.38	0.00	2.66	73.10	0.04	9.08	0.64	0.00	7
1.51609	15.01	0.00	2.51	73.05	0.05	8.83	0.53	0.00	7
1.51508	15.15	0.00	2.25	73.50	0.00	8.34	0.63	0.00	7
1.51653	11.95	0.00	1.19	75.18	2.70	8.93	0.00	0.00	7
1.51514	14.85	0.00	2.42	73.72	0.00	8.39	0.56	0.00	7
1.51658	14.80	0.00	1.99	73.11	0.00	8.28	1.71	0.00	7
1.51617	14.95	0.00	2.27	73.30	0.00	8.71	0.67	0.00	7
1.51732	14.95	0.00	1.80	72.99	0.00	8.61	1.55	0.00	7
1.51645	14.94	0.00	1.87	73.11	0.00	8.67	1.38	0.00	7
1.51831	14.39	0.00	1.82	72.86	1.41	6.47	2.88	0.00	7
1.51640	14.37	0.00	2.74	72.85	0.00	9.45	0.54	0.00	7
1.51623	14.14	0.00	2.88	72.61	0.08	9.18	1.06	0.00	7
1.51685	14.92	0.00	1.99	73.06	0.00	8.40	1.59	0.00	7
1.52065	14.36	0.00	2.02	73.42	0.00	8.44	1.64	0.00	7
1.51651	14.38	0.00	1.94	73.61	0.00	8.48	1.57	0.00	7
1.51711	14.23	0.00	2.08	73.36	0.00	8.62	1.67	0.00	7

1. Using visualizations, explore the predictor variables to understand their distributions as well as the relationships between predictors.

Descriptive Summary Statistics of the Predictors:

# Excluding the classifier target variable from the Glass data frame to focus on predictors only

GlassPredsDF <- Glass[,-10]

stat_desc <- function(df){
df %>% 
    describe() %>%
    kbl() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
    scroll_box(width="100%",height="350px")
}

stat_desc(GlassPredsDF)

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
RI	1	214	1.5183654	0.0030369	1.51768	1.5180119	0.0018755	1.51115	1.53393	0.02278	1.6027151	4.7167266	0.0002076
Na	2	214	13.4078505	0.8166036	13.30000	13.3768023	0.6449310	10.73000	17.38000	6.65000	0.4478343	2.8979666	0.0558219
Mg	3	214	2.6845327	1.4424078	3.48000	2.8655233	0.3039330	0.00000	4.49000	4.49000	-1.1364523	-0.4526762	0.0986010
Al	4	214	1.4449065	0.4992696	1.36000	1.4122093	0.3113460	0.29000	3.50000	3.21000	0.8946104	1.9383534	0.0341294
Si	5	214	72.6509346	0.7745458	72.79000	72.7073256	0.5708010	69.81000	75.41000	5.60000	-0.7202392	2.8163627	0.0529469
K	6	214	0.4970561	0.6521918	0.55500	0.4318023	0.1704990	0.00000	6.21000	6.21000	6.4600889	52.8665268	0.0445829
Ca	7	214	8.9569626	1.4231535	8.60000	8.7421512	0.6597570	5.43000	16.19000	10.76000	2.0184463	6.4104000	0.0972848
Ba	8	214	0.1750467	0.4972193	0.00000	0.0337791	0.0000000	0.00000	3.15000	3.15000	3.3686800	12.0801412	0.0339892
Fe	9	214	0.0570093	0.0974387	0.00000	0.0358140	0.0000000	0.00000	0.51000	0.51000	1.7298107	2.5203615	0.0066608

I have use the describe() method of the psych package to review all the baseline statistical metrics for the predictors. From the table above, it can be noted that chemicals like Fe, Ba and K has near zero mean values and there is notable skewness in data for many of the predictors.

Descriptive Statistical Plots

Box Plot:

gb1 <- ggplot(data = GlassPredsDF, aes(y = RI)) + geom_boxplot()
gb2 <- ggplot(data = GlassPredsDF, aes(y = Na)) + geom_boxplot()
gb3 <- ggplot(data = GlassPredsDF, aes(y = Mg)) + geom_boxplot()
gb4 <- ggplot(data = GlassPredsDF, aes(y = Al)) + geom_boxplot()
gb5 <- ggplot(data = GlassPredsDF, aes(y = Si)) + geom_boxplot()
gb6 <- ggplot(data = GlassPredsDF, aes(y = K)) + geom_boxplot()
gb7 <- ggplot(data = GlassPredsDF, aes(y = Ca)) + geom_boxplot()
gb8 <- ggplot(data = GlassPredsDF, aes(y = Ba)) + geom_boxplot()
gb9 <- ggplot(data = GlassPredsDF, aes(y = Fe)) + geom_boxplot()

title <- ggdraw() +
  draw_label(
    "Box Plots of Glass Predictors",
    fontface = 'bold',
    x = 0,
    hjust = 0
  ) +  theme(
    # add margin on the left of the drawing canvas,
    # so title is aligned with left edge of first plot
    plot.margin = margin(0, 0, 0, 7)
  )

plot_row <- plot_grid(gb1, gb2, gb3, gb4, gb5, gb6, gb7, gb8, gb9, labels = colnames(GlassPredsDF), label_size = 12)

plot_grid(
  title, plot_row,
  ncol = 1,
  # rel_heights values control vertical title margins
  rel_heights = c(0.1, 1)
)

Density Plots

GlassPredsDF %>% 
  gather(variable, value) %>%
  ggplot(., aes(value)) + 
  geom_density(fill = "dodgerblue4", color="dodgerblue4") + 
  facet_wrap(~variable, scales ="free", ncol = 4) +
  labs(x = element_blank(), y = element_blank())

Correlation Plot

corrMatrix <- round(cor(GlassPredsDF),4)

corrMatrix %>% corrplot(., method = "color", outline = T, addgrid.col = "darkgray", order="hclust", addrect = 4, rect.col = "black", rect.lwd = 5,cl.pos = "b", tl.col = "indianred4", tl.cex = 1.0, cl.cex = 1.0, addCoef.col = "white", number.digits = 2, number.cex = 0.8, col = colorRampPalette(c("darkred","white","dodgerblue4"))(100))

Pairwise Plot

pairs.panels(GlassPredsDF, 
             method = "pearson", # correlation method
             hist.col = "dodgerblue4",
             density = TRUE,  # show density plots
             ellipses = TRUE # show correlation ellipses
             )

From the above plot, it can be observed that Refractive Index (RI) and % of Calcium content (Ca) has a high correlation score of 0.81.

2. Do there appear to be any outliers in the data? Are any predictors skewed?

Observations:

• From the Box plot of predictors, it can be observed that apart from % of Mg content, all other predictors have quite a few outliers in the data.
• For majority of the records,% content of Barium (Ba) are recorded as zeros. It's hard to say whether these are misssing values or truly recorded to be absent in most of the observations. So due to ~0 mean, records with Ba content > 0 are appearing as outliers.
• Similar to Barium (Ba), Iron (Fe) and Potassium (K) % have similar pattern.
• From the density plot of the predictors, it can be observed that majority of the predictors have some degree of skewness -
• Ba, Ca, Fe, K and RI have right skewness
• Mg and Si have little bit of left skewness

3. Are there any relevant transformations of one or more predictors that might improve the classification model?

trans <- preProcess(GlassPredsDF, method = c("BoxCox", "center", "scale"))

trans

## Created from 214 samples and 9 variables
## 
## Pre-processing:
##   - Box-Cox transformation (5)
##   - centered (9)
##   - ignored (0)
##   - scaled (9)
## 
## Lambda estimates for Box-Cox transformation:
## -2, -0.1, 0.5, 2, -1.1

Transformation Deterination for Predictors:

Below is an analysis of each predictor and applicable transformations. Wher applicable (values >0), I have used the boxcoxfit() function from geoR package teh determine the value of lambda.

RI:

boxcoxfit(GlassPredsDF$RI)

## Fitted parameters:
##        lambda          beta       sigmasq 
## -4.999944e+00  1.752172e-01  5.999846e-08 
## 
## Convergence code returned by optim: 0

The suggested value of lambda for RI is -5.

GlassPredsDF$RI_Trans <- (GlassPredsDF$RI^(-5) - 1)/(-5)

plot1 <-ggplot(GlassPredsDF, aes(x=RI)) + geom_histogram(alpha=0.5) + ggtitle("RI Original")
plot2 <-ggplot(GlassPredsDF, aes(x=RI_Trans)) + geom_histogram(alpha=0.5) + ggtitle("RI Transformed")

grid.arrange(plot1,plot2, ncol=2) 

Al:

boxcoxfit(GlassPredsDF$Al)

## Fitted parameters:
##    lambda      beta   sigmasq 
## 0.4872856 0.3667122 0.1687402 
## 
## Convergence code returned by optim: 0

The suggested value of lambda for Al is 0.4872856. I am going to make a square root transformation for Al

GlassPredsDF$Al_Trans <- (GlassPredsDF$Al^(0.5) - 1)/(0.5)

plot1 <-ggplot(GlassPredsDF, aes(x=Al)) + geom_histogram(alpha=0.5) + ggtitle("Al Original")
plot2 <-ggplot(GlassPredsDF, aes(x=Al_Trans)) + geom_histogram(alpha=0.5) + ggtitle("Al Transformed")

grid.arrange(plot1,plot2, ncol=2) 

Fe:

Considering the highly left skewed data, I am going to do a natural log transformation.

GlassPredsDF$Fe_Trans <- log(GlassPredsDF$Fe) 

plot1 <-ggplot(GlassPredsDF, aes(x=Fe)) + geom_histogram(alpha=0.5) + ggtitle("Fe Original")
plot2 <-ggplot(GlassPredsDF, aes(x=Fe_Trans)) + geom_histogram(alpha=0.5) + ggtitle("Fe Transformed")

grid.arrange(plot1,plot2, ncol=2) 

## Warning: Removed 144 rows containing non-finite values (stat_bin).

Ba:

Considering the highly left skewed data, I am going to do a natural log transformation.

GlassPredsDF$Ba_Trans <- log(GlassPredsDF$Ba) 

plot1 <-ggplot(GlassPredsDF, aes(x=Ba)) + geom_histogram(alpha=0.5) + ggtitle("Ba Original")
plot2 <-ggplot(GlassPredsDF, aes(x=Ba_Trans)) + geom_histogram(alpha=0.5) + ggtitle("Ba Transformed")

grid.arrange(plot1,plot2, ncol=2) 

## Warning: Removed 176 rows containing non-finite values (stat_bin).

K:

Considering the highly left skewed data, I am going to do a natural log transformation.

GlassPredsDF$K_Trans <- log(GlassPredsDF$K) 

plot1 <-ggplot(GlassPredsDF, aes(x=K)) + geom_histogram(alpha=0.5) + ggtitle("K Original")
plot2 <-ggplot(GlassPredsDF, aes(x=K_Trans)) + geom_histogram(alpha=0.5) + ggtitle("K Transformed")

grid.arrange(plot1,plot2, ncol=2) 

## Warning: Removed 30 rows containing non-finite values (stat_bin).

Exercise 3.2

The soybean data can also be found at the UC Irvine Machine Learning Repository. Data were collected to predict disease in 683 soybeans. The 35 predictors are mostly categorical and include information on the environmental conditions (e.g., temperature, precipitation) and plant conditions (e.g., left spots, mold growth). The outcome labels consist of 19 distinct classes.

Data Description:

There are 19 classes, only the first 15 of which have been used in prior work. The folklore seems to be that the last four classes are unjustified by the data since they have so few examples. There are 35 categorical attributes, some nominal and some ordered. The value "dna" means does not apply. The values for attributes are encoded numerically, with the first value encoded as "0," the second as "1," and so forth.

Data Format:

A data frame with 683 observations on 36 variables. There are 35 categorical attributes, all numerical and a nominal denoting the class:

data(Soybean)
str(Soybean)

## 'data.frame':    683 obs. of  36 variables:
##  $ Class          : Factor w/ 19 levels "2-4-d-injury",..: 11 11 11 11 11 11 11 11 11 11 ...
##  $ date           : Factor w/ 7 levels "0","1","2","3",..: 7 5 4 4 7 6 6 5 7 5 ...
##  $ plant.stand    : Ord.factor w/ 2 levels "0"<"1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ precip         : Ord.factor w/ 3 levels "0"<"1"<"2": 3 3 3 3 3 3 3 3 3 3 ...
##  $ temp           : Ord.factor w/ 3 levels "0"<"1"<"2": 2 2 2 2 2 2 2 2 2 2 ...
##  $ hail           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
##  $ crop.hist      : Factor w/ 4 levels "0","1","2","3": 2 3 2 2 3 4 3 2 4 3 ...
##  $ area.dam       : Factor w/ 4 levels "0","1","2","3": 2 1 1 1 1 1 1 1 1 1 ...
##  $ sever          : Factor w/ 3 levels "0","1","2": 2 3 3 3 2 2 2 2 2 3 ...
##  $ seed.tmt       : Factor w/ 3 levels "0","1","2": 1 2 2 1 1 1 2 1 2 1 ...
##  $ germ           : Ord.factor w/ 3 levels "0"<"1"<"2": 1 2 3 2 3 2 1 3 2 3 ...
##  $ plant.growth   : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
##  $ leaves         : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
##  $ leaf.halo      : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
##  $ leaf.marg      : Factor w/ 3 levels "0","1","2": 3 3 3 3 3 3 3 3 3 3 ...
##  $ leaf.size      : Ord.factor w/ 3 levels "0"<"1"<"2": 3 3 3 3 3 3 3 3 3 3 ...
##  $ leaf.shread    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ leaf.malf      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ leaf.mild      : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stem           : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
##  $ lodging        : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 1 1 1 ...
##  $ stem.cankers   : Factor w/ 4 levels "0","1","2","3": 4 4 4 4 4 4 4 4 4 4 ...
##  $ canker.lesion  : Factor w/ 4 levels "0","1","2","3": 2 2 1 1 2 1 2 2 2 2 ...
##  $ fruiting.bodies: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
##  $ ext.decay      : Factor w/ 3 levels "0","1","2": 2 2 2 2 2 2 2 2 2 2 ...
##  $ mycelium       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ int.discolor   : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
##  $ sclerotia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ fruit.pods     : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ fruit.spots    : Factor w/ 4 levels "0","1","2","4": 4 4 4 4 4 4 4 4 4 4 ...
##  $ seed           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ mold.growth    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ seed.discolor  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ seed.size      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ shriveling     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ roots          : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...

**Preview of the Data:**

Soybean %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Class	date	plant.stand	precip	temp	hail	crop.hist	area.dam	sever	seed.tmt	germ	plant.growth	leaves	leaf.halo	leaf.marg	leaf.size	leaf.shread	leaf.malf	leaf.mild	stem	lodging	stem.cankers	canker.lesion	fruiting.bodies	ext.decay	mycelium	int.discolor	sclerotia	fruit.pods	fruit.spots	seed	mold.growth	seed.discolor	seed.size	shriveling	roots
diaporthe-stem-canker	6	0	2	1	0	1	1	1	0	0	1	1	0	2	2	0	0	0	1	1	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	4	0	2	1	0	2	0	2	1	1	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	3	0	2	1	0	1	0	2	1	2	1	1	0	2	2	0	0	0	1	0	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	3	0	2	1	0	1	0	2	0	1	1	1	0	2	2	0	0	0	1	0	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	6	0	2	1	0	2	0	1	0	2	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	5	0	2	1	0	3	0	1	0	1	1	1	0	2	2	0	0	0	1	0	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	5	0	2	1	0	2	0	1	1	0	1	1	0	2	2	0	0	0	1	1	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	4	0	2	1	1	1	0	1	0	2	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	6	0	2	1	0	3	0	1	1	1	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	4	0	2	1	0	2	0	2	0	2	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
charcoal-rot	6	0	0	2	0	1	3	1	1	0	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	4	0	0	1	1	1	3	1	1	1	1	1	0	2	2	0	0	0	1	1	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	3	0	0	1	0	1	2	1	0	0	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	6	0	0	1	1	3	3	1	1	0	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	6	0	0	2	0	1	3	1	1	1	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	5	0	0	2	1	3	3	1	1	2	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	6	0	0	2	1	0	2	1	0	0	1	1	0	2	2	0	0	0	1	1	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	4	0	0	1	0	2	2	1	0	1	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	3	0	0	2	0	2	2	1	0	2	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	5	0	0	2	1	2	2	1	0	2	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
rhizoctonia-root-rot	1	1	2	0	0	2	1	2	0	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	1	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	1	1	2	0	0	1	1	2	0	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	3	0	2	0	1	3	1	2	0	1	1	0	0	2	2	0	0	0	1	1	1	1	0	1	1	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	0	1	2	0	0	0	1	1	1	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	0	1	2	0	0	1	1	2	1	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	1	1	2	0	0	3	1	2	0	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	1	1	2	0	0	0	1	1	0	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	2	1	2	0	0	2	1	1	0	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	1	1	2	0	0	1	1	2	0	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	2	1	2	0	0	1	1	2	0	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	0	1	2	1	1	1	1	1	0	0	1	1	0	2	2	0	0	0	1	0	1	2	0	1	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	1	1	2	1	NA	3	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	0	0	2	1	2	1	1	1	1	0	2	2	0	0	0	1	0	2	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	2	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	2	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	0	1	1	1	0	1	1	1	0	0	1	1	0	2	2	0	0	0	1	0	1	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	3	1	2	0	0	2	1	2	1	1	1	1	0	2	2	0	0	0	1	0	2	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	2	1	1	1	NA	0	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	0	0	1	1	2	0	1	1	1	0	2	2	0	0	0	1	0	1	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	2	1	2	1	NA	1	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	1	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	0	1	2	1	0	3	1	1	0	0	1	1	0	2	2	0	0	0	1	0	1	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	0	1	1	1	1	2	1	2	1	0	1	1	0	2	2	0	0	0	1	1	2	2	0	1	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	3	1	2	0	0	1	1	2	1	0	1	1	0	2	2	0	0	0	1	0	2	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	2	1	2	2	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	0	1	2	1	0	2	1	1	0	1	1	1	0	2	2	0	0	0	1	0	1	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	2	1	1	2	NA	2	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	1	1	1	1	2	0	2	1	1	0	2	2	0	0	0	1	0	1	2	0	1	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	0	1	2	1	0	3	1	1	0	2	1	1	0	2	2	0	0	0	1	0	1	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	1	1	2	1	0	0	1	2	1	1	1	1	0	2	2	0	0	0	1	0	2	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	1	1	2	1	NA	0	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	1	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	2	1	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	1	NA	1	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	1	1	3	1	2	0	1	1	1	0	2	2	0	0	0	1	1	1	2	0	1	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	3	1	1	1	NA	3	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	2	NA	2	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	3	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	0	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	0	0	1	1	2	0	0	1	1	0	2	2	0	0	0	1	0	1	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	3	1	1	1	NA	1	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	0	0	0	1	2	1	0	1	1	0	2	2	0	0	0	1	0	2	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	3	1	2	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	2	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	0	0	2	1	2	1	2	1	1	0	2	2	0	0	0	1	0	2	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	3	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
brown-stem-rot	4	0	0	1	0	1	3	1	1	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	1	3	1	1	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	3	1	0	0	0	3	0	1	1	2	1	0	0	2	2	0	0	0	1	0	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	0	0	2	0	1	3	1	1	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	2	0	2	3	1	1	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	3	2	1	0	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	1	1	3	3	1	0	2	1	1	2	0	1	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	6	0	1	1	1	2	0	1	1	0	1	0	0	2	1	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	1	0	0	0	3	2	1	0	0	1	1	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	1	0	1	0	1	3	1	1	0	1	1	2	0	1	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	4	0	1	0	1	2	3	1	1	2	1	0	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	4	1	0	0	0	3	2	1	1	1	1	1	2	0	1	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	2	0	1	1	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	3	1	0	0	0	2	0	2	0	1	1	1	2	0	1	0	0	0	1	0	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	0	0	1	0	3	2	1	0	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	3	3	1	1	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	3	2	1	0	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	1	2	1	1	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	1	2	1	0	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	3	0	0	1	0	3	2	1	0	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
powdery-mildew	5	0	0	1	1	3	3	1	0	1	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	6	0	1	0	1	0	0	0	1	2	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	1	1	0	1	0	3	3	1	2	0	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	6	1	1	0	0	2	2	0	1	2	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	4	1	1	0	0	2	2	0	2	0	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	6	0	0	1	1	1	1	1	0	2	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	2	1	1	0	0	2	2	0	0	1	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	6	1	0	1	0	1	1	1	1	2	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	5	1	0	1	0	1	1	1	0	1	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	1	1	0	1	0	1	1	1	2	0	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
downy-mildew	6	0	2	0	1	2	1	0	1	2	0	1	2	0	1	0	1	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	2	0	2	1	1	1	1	1	1	2	0	1	2	0	1	0	1	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	1	0	2	1	1	3	2	1	0	1	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	4	1	2	2	0	2	2	1	0	1	0	1	1	0	1	0	1	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	1	0	2	0	1	0	0	1	0	1	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	2	1	2	0	0	3	0	1	0	1	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	2	1	2	1	0	2	0	1	0	1	0	1	2	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	4	1	2	2	0	2	1	0	1	2	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	4	1	2	0	0	1	2	1	0	1	0	1	2	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	5	1	2	1	0	3	2	1	0	1	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
brown-spot	1	1	2	2	1	3	3	1	0	2	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	3	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	3	1	1	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	1	0	1	2	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	2	1	3	3	1	1	1	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	2	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	0	1	2	2	1	3	3	1	2	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	3	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	2	3	1	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	2	1	0	3	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	5	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	3	3	1	1	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	3	3	1	2	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	4	0	2	1	0	1	3	1	0	0	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	2	3	1	0	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	4	1	2	1	0	3	3	1	0	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	3	3	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	0	1	1	1	1	2	2	0	2	1	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	1	1	1	2	0	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	1	0	1	2	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	1	3	1	0	0	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	3	3	1	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	2	3	2	2	1	0	1	2	0	1	0	0	0	1	0	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	2	2	1	3	1	1	1	0	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	2	3	1	2	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	2	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	5	0	2	1	0	1	3	1	0	0	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	4	1	1	1	1	2	2	0	0	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	1	0	0	0	0	0	0
brown-spot	3	1	2	1	0	1	3	1	0	2	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	3	3	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	4	0	2	1	0	2	3	2	1	1	0	1	2	0	1	0	0	0	1	0	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	2	1	0	2	3	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	1	1	1	0	0	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	1	2	1	0	3	1	1	0	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	3	3	2	2	0	0	1	2	0	1	0	0	0	1	0	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	3	1	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	1	2	1	0	3	1	1	0	2	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	2	1	0	3	3	2	2	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	5	1	2	1	0	3	3	2	0	2	0	1	2	0	1	0	0	0	1	0	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	5	0	2	1	1	3	3	1	1	0	0	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	4	0	2	2	1	2	3	1	1	1	0	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	2	0	1	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	3	0	1	1	0	1	2	0	0	0	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	3	0	1	1	0	3	2	0	0	0	0	1	1	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	3	0	2	1	1	2	1	1	1	0	0	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	3	0	1	1	0	1	0	0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	4	0	2	1	1	0	3	1	1	1	0	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	2	0	1	1	0	3	1	0	0	0	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	4	1	2	2	1	2	1	1	1	2	0	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-pustule	2	1	1	2	0	2	2	0	0	2	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2
bacterial-pustule	3	0	2	0	1	2	3	1	1	1	1	1	2	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0
bacterial-pustule	2	0	1	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-pustule	4	1	2	1	0	3	0	1	0	2	0	1	1	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1
bacterial-pustule	3	0	2	1	1	1	1	1	0	1	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1
bacterial-pustule	3	1	1	0	0	2	0	0	0	2	0	1	2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
bacterial-pustule	3	0	1	1	1	2	3	0	0	1	1	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0
bacterial-pustule	3	1	2	1	0	0	2	1	0	2	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1
bacterial-pustule	4	0	1	1	1	1	3	0	0	1	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0
bacterial-pustule	5	1	1	1	0	2	0	0	1	2	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
purple-seed-stain	6	0	2	0	1	2	2	0	0	0	0	0	0	2	2	0	0	0	1	1	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	6	0	2	0	0	2	2	0	1	1	0	1	2	0	0	0	0	0	1	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	4	0	2	1	1	1	1	0	1	2	0	0	0	2	2	0	0	0	0	0	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	4	0	2	1	1	0	0	0	0	1	0	1	2	0	0	0	0	0	0	1	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	4	0	2	0	0	0	0	0	0	2	0	1	2	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	6	0	2	2	0	2	2	0	0	1	0	1	2	0	0	0	0	0	1	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	3	0	2	0	1	0	0	0	0	1	0	0	0	2	2	0	0	0	0	1	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	3	0	2	1	1	3	3	0	1	1	0	0	0	2	2	0	0	0	0	0	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	5	0	2	1	0	1	1	0	0	0	0	1	2	0	0	0	0	0	1	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	4	0	2	1	0	0	0	0	1	1	0	0	0	2	2	0	0	0	1	0	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
anthracnose	5	1	2	1	0	3	3	1	1	0	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	1	2	2	1	2	2	0	1	2	0	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	1	2	1	1	1	0	1	0
anthracnose	6	0	2	1	0	1	1	1	1	1	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	2	1	2	2	1	0	0	1	0	0	1	1	0	2	2	0	0	0	1	0	2	1	0	1	0	0	0	0	0	0	0	0	0	0	0
anthracnose	3	0	2	1	0	3	3	1	0	0	1	1	0	2	2	0	0	0	1	0	3	2	1	1	0	0	0	1	2	1	1	1	0	0	0
anthracnose	4	1	2	2	1	2	2	1	0	1	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	6	0	2	1	0	2	2	1	0	1	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	1	0	2	1	0	1	1	1	1	1	1	1	0	2	2	0	0	0	1	0	2	2	0	1	0	0	0	0	0	1	0	1	0	0	0
anthracnose	6	1	2	1	0	2	2	1	1	1	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	5	0	2	1	0	1	1	1	2	2	1	1	0	2	2	0	0	0	1	0	3	2	1	1	0	0	0	1	2	1	1	1	0	0	0
anthracnose	5	1	2	2	1	3	3	0	1	2	1	1	0	2	2	0	0	0	1	1	3	2	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	0	0	2	1	0	3	3	1	1	2	1	1	0	2	2	0	0	0	1	0	2	2	0	1	0	0	0	0	0	1	1	0	1	0	0
anthracnose	6	0	2	1	0	2	2	0	0	0	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	1	2	1	0	1	1	1	0
anthracnose	5	1	2	1	0	1	1	1	0	1	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	5	0	2	1	0	2	2	1	0	2	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	0	0	0	0	0	0
anthracnose	6	1	2	2	1	0	0	1	0	1	0	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	0	2	1	0	1	1	1	0	0	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	6	1	2	2	1	3	3	0	2	1	0	1	0	2	2	0	0	0	1	1	3	2	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	1	2	1	0	3	3	1	0	1	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	5	1	2	1	0	2	2	1	1	1	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	1	1	0	1	1	0
phyllosticta-leaf-spot	3	1	1	1	0	0	2	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	3	0	0	1	1	0	2	0	0	1	0	1	2	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	3	1	1	1	0	0	0	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	3	0	0	1	1	2	0	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	3	1	1	2	0	3	2	0	1	1	0	1	2	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	0	0	1	1	0	3	0	2	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	1	0	0	2	1	3	2	1	1	1	0	1	2	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	1	1	1	0	2	2	1	1	1	0	1	2	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	0	0	2	1	3	0	1	1	0	1	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	1	1	2	0	3	3	0	2	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	1	2	1	0	1	1	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	1	1	0	3	3	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	3	0	2	1	0	0	0	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	3	3	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	1	1	1	2	2	0	2	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	5	0	2	2	0	3	3	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	1	1	0	3	3	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	1	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	3	3	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	3	2	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	3	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	1	2	1	0	3	0	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	1	0	1	1	0	1	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	2	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	1	0	0	0	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	2	1	0	2	2	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	2	1	0	1	1	1	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	1	0	2	1	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	3	2	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	1	2	2	0	1	1	0	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	1	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	3	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	1	2	1	0	2	1	0	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	1	1	2	0	2	2	0	2	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	4	1	2	1	0	1	2	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	1	2	2	0	2	1	0	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	1	2	1	0	0	3	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	2	2	0	3	3	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	3	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	3	0	2	1	0	0	0	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	1	0	1	2	0	1	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	1	1	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	2	2	0	1	1	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	1	0	3	3	0	1	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	1	0	2	1	0	0	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	1	0	0	3	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	1	0	0	3	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	2	1	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	1	0	3	0	0	1	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	1	0	1	2	0	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	1	2	0	3	3	0	0	0	0	1	2	0	1	0	0	0	1	0	3	2	1	1	0	0	0	1	2	1	0	0	0	0	0
frog-eye-leaf-spot	4	0	1	2	0	1	1	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	1	1	0	2	1	0	0	0	0	1	2	0	1	0	0	0	1	0	3	1	0	1	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	1	0	3	2	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	6	1	2	2	0	3	3	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	1	1	0	3	3	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	1	0	2	3	0	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	2	2	0	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	1	1	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	2	3	0	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	0	1	0	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	3	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	0	1	1	1	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	1	1	1	1	0	1	2	0	1	0	0	0	1	0	3	1	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	3	1	2	1	0	3	2	1	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	3	0	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	1	1	0	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	0	1	2	1	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	2	1	0	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	3	0	1	0	0	0	1	2	0	1	0	0	0	1	0	3	1	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	1	0	1	2	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	1	2	0	3	3	0	1	0	0	1	2	0	1	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	0	1	1	1	0
frog-eye-leaf-spot	5	0	1	1	0	1	3	1	2	0	0	1	2	0	1	0	0	0	1	0	3	0	1	0	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	3	2	1	0	0	0	1	2	0	1	0	0	0	1	0	3	1	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	1	0	3	3	0	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	3	1	2	1	0	3	0	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	6	1	2	2	0	3	1	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	1	2	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	0	1	0	1	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	1	2	2	0	3	0	0	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	2	0	3	3	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	0	1	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	3	1	1	1	0	1	2	0	1	0	0	0	1	0	3	1	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	1	1	2	0	1	1	0	2	2	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	0	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	1	0	1	2	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	0	1	3	1	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	1	2	0	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	2	0	0	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	1	0	2	3	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
diaporthe-pod-&-stem-blight	5	0	2	2	NA	3	3	NA	NA	0	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	0	1	1	1	1	NA
diaporthe-pod-&-stem-blight	6	0	2	2	NA	2	3	NA	NA	1	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	5	0	2	2	NA	3	3	NA	NA	0	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	1	1	1	2	NA	3	0	NA	NA	2	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	0	1	1	1	1	NA
diaporthe-pod-&-stem-blight	5	NA	2	2	NA	2	3	NA	NA	NA	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	5	0	2	2	NA	2	3	NA	NA	0	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
cyst-nematode	2	NA	NA	NA	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	3	NA	NA	NA	NA	3	2	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	4	NA	NA	NA	NA	3	2	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	3	NA	NA	NA	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	3	NA	NA	NA	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	4	NA	NA	NA	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
2-4-d-injury	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
herbicide-injury	1	1	NA	0	NA	1	0	NA	NA	NA	1	1	2	1	1	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1
herbicide-injury	0	1	NA	0	NA	0	3	NA	NA	NA	1	1	0	2	2	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1
herbicide-injury	1	1	NA	0	NA	0	0	NA	NA	NA	1	1	0	2	2	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1
herbicide-injury	1	1	NA	0	NA	1	3	NA	NA	NA	1	1	2	1	1	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1
diaporthe-stem-canker	6	0	2	1	0	1	0	1	0	2	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	3	0	2	1	0	2	0	2	1	1	1	1	0	2	2	0	0	0	1	0	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	4	0	2	1	0	3	0	2	0	2	1	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	5	0	2	1	0	1	0	1	0	1	1	1	0	2	2	0	0	0	1	0	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	3	0	2	1	0	3	0	1	0	1	1	1	0	2	2	0	0	0	1	0	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	5	0	2	1	0	2	0	1	1	0	1	1	0	2	2	0	0	0	1	0	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	5	0	2	1	0	3	1	1	1	2	1	1	0	2	2	0	0	0	1	1	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	3	0	2	1	0	2	1	1	0	1	1	1	0	2	2	0	0	0	1	1	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	4	0	2	1	0	3	0	1	1	2	1	1	0	2	2	0	0	0	1	1	3	0	1	1	0	0	0	0	4	0	0	0	0	0	0
diaporthe-stem-canker	6	0	2	1	0	3	0	1	1	1	1	1	0	2	2	0	0	0	1	1	3	1	1	1	0	0	0	0	4	0	0	0	0	0	0
charcoal-rot	4	0	0	1	0	2	3	1	1	1	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	5	0	0	2	0	3	2	1	0	2	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	4	0	0	2	0	3	3	1	1	1	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	5	0	0	2	0	0	2	1	0	2	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	5	0	0	2	1	2	2	1	0	2	1	1	0	2	2	0	0	0	1	1	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	3	0	0	2	1	0	2	1	0	1	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	4	0	0	2	1	1	3	1	1	2	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	5	0	0	2	1	2	2	1	0	0	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	6	0	0	2	1	3	3	1	1	1	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
charcoal-rot	6	0	0	2	1	3	3	1	1	0	1	1	0	2	2	0	0	0	1	0	0	3	0	0	0	2	1	0	4	0	0	0	0	0	0
rhizoctonia-root-rot	0	1	2	0	0	0	1	1	0	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	1
rhizoctonia-root-rot	0	1	2	0	0	3	1	2	0	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	0	1	2	0	0	2	1	1	1	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	2	1	2	0	0	0	1	1	0	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	1	1	2	0	0	2	1	1	0	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	2	1	2	0	0	3	1	2	0	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	0	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	2	1	2	0	0	2	1	1	0	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	1	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	4	0	2	0	1	0	1	2	0	2	1	1	0	2	2	0	0	0	1	1	1	1	0	1	1	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	0	1	2	0	0	1	1	1	1	1	1	0	0	2	2	0	0	0	1	0	1	1	0	1	1	0	0	3	4	0	0	0	0	0	0
rhizoctonia-root-rot	2	1	2	0	0	3	1	2	0	2	1	0	0	2	2	0	0	0	1	0	1	1	0	1	1	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	2	1	1	0	0	3	1	2	0	2	1	1	0	2	2	0	0	0	1	0	1	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	1	1	2	0	0	3	1	1	1	2	1	1	0	2	2	0	0	0	1	0	2	2	0	0	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	2	1	2	1	1	3	1	2	1	2	1	1	0	2	2	0	0	0	1	0	2	2	0	1	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	1	1	2	1	1	2	3	1	1	1	1	1	0	2	2	0	0	0	1	0	2	2	0	1	0	0	0	3	4	0	0	0	0	0	0
phytophthora-rot	3	1	1	1	NA	2	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	3	1	NA	NA	NA	1	1	0	2	2	0	0	0	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	0	1	2	2	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	1	NA	0	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	0	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	4	1	1	2	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	4	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	1	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	1	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	0	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	4	1	1	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	0	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	4	1	1	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	0	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	0	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	2	1	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	0	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	4	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	4	1	1	1	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	1	1	2	2	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	2	2	NA	2	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	2	2	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	2	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
phytophthora-rot	3	1	1	1	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	1	NA	3	2	NA	0	0	0	0	NA	NA	NA	NA	NA	NA	NA	1
brown-stem-rot	3	0	0	0	1	1	0	2	0	0	1	1	2	0	1	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	4	0	1	0	1	2	2	1	1	1	1	0	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	3	0	0	0	1	3	2	2	0	1	1	1	2	0	1	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	0	0	1	1	3	0	1	0	0	1	1	2	0	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	6	0	0	1	1	2	1	1	1	1	1	1	2	0	1	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	4	0	1	0	1	3	2	2	0	2	1	0	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	0	0	2	0	2	3	1	0	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	1	0	1	0	1	1	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	3	2	1	0	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	1	0	2	3	1	0	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	1	0	2	2	1	1	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	3	1	1	0	0	2	0	2	0	2	1	0	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	1	1	1	0	2	3	2	0	0	1	0	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	4	1	0	0	0	2	2	2	0	1	1	1	2	0	1	0	0	0	1	0	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	5	1	1	0	0	3	3	1	1	2	1	0	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	4	1	1	0	0	3	1	1	1	0	1	0	0	2	2	0	0	0	1	0	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	6	1	1	1	0	3	3	1	1	1	1	0	0	2	2	0	0	0	1	1	0	3	0	0	0	1	0	0	4	0	0	0	0	0	0
brown-stem-rot	3	0	0	1	0	2	2	1	0	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	2	0	3	0	1	0	2	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	3	0	0	1	0	2	2	1	1	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	1	3	1	0	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	2	0	2	0	1	1	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	4	0	0	1	0	1	0	1	0	0	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
brown-stem-rot	5	0	0	2	0	3	2	1	1	1	0	1	0	2	2	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
powdery-mildew	1	0	0	0	1	0	0	0	0	0	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	2	0	1	1	1	1	1	1	1	1	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	3	0	2	0	1	2	2	0	0	2	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	4	0	0	1	1	3	3	1	0	0	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	5	0	1	0	1	0	0	0	0	1	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	4	0	1	0	1	2	2	0	1	0	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	3	1	0	1	0	3	3	1	1	2	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	4	1	1	0	0	0	0	0	2	0	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	2	1	1	0	0	0	0	0	0	1	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
powdery-mildew	5	1	0	1	0	3	3	1	0	1	0	1	0	2	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
downy-mildew	3	0	2	0	1	2	2	1	0	1	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	4	0	2	1	1	3	3	1	1	2	0	1	2	0	1	0	1	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	5	0	2	1	1	1	0	1	0	1	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	2	0	2	0	1	0	3	1	1	2	0	1	2	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	3	0	2	1	1	1	0	1	0	1	0	1	2	0	1	0	1	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	6	1	2	0	0	1	3	0	1	2	0	1	2	0	1	0	1	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	3	1	2	2	0	3	1	1	1	2	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	5	1	2	1	0	2	3	0	1	2	0	1	1	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	3	1	2	1	0	1	1	0	1	2	0	1	2	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
downy-mildew	5	1	2	0	0	3	3	0	1	2	0	1	2	0	1	0	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
brown-spot	1	1	2	1	0	3	2	1	0	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	1	1	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	2	3	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	2	1	0	3	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	2	1	0	1	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	5	0	2	1	0	2	3	2	1	1	0	1	2	0	1	0	0	0	1	0	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	3	3	1	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	2	3	1	2	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	1	1	1	2	2	1	1	2	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	1	2	2	1	1	1	0	2	1	0	1	2	0	1	0	0	0	1	0	3	1	0	0	0	0	0	1	2	0	0	0	0	0	0
brown-spot	0	0	1	1	0	0	0	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	2	0	1	1	0	1	1	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	1	1	0	2	2	1	2	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	2	0	3	3	0	0	1	1	1	2	0	1	0	0	0	1	0	3	1	0	0	0	0	0	0	1	0	0	0	0	0	0
brown-spot	4	0	1	1	0	1	0	1	1	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	1	2	0	0	0	0	0	0
brown-spot	0	0	2	2	0	2	1	1	2	1	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	1	1	0	3	2	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	2	0	1	3	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	0	0	1	1	0	2	0	0	2	2	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	2	1	3	1	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	1	1	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	2	1	0	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	1	2	1	0	3	3	1	0	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	4	0	2	1	0	2	1	1	0	0	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	5	0	2	1	0	3	2	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	2	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	3	3	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	2	3	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	3	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	1	1	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	3	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	3	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	3	3	1	0	1	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	1	2	1	0	2	1	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	3	2	1	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	2	3	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	4	1	2	1	0	3	1	1	0	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	5	0	2	1	0	1	2	1	0	0	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	2	3	1	0	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	1	2	1	0	3	3	1	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	0	2	1	0	2	1	1	0	0	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	4	0	2	1	0	3	3	1	0	1	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	5	1	2	1	0	3	3	1	0	2	0	1	2	0	1	0	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	1	1	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	1	2	1	0	3	3	1	0	2	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	1	1	1	0	0	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	2	1	0	1	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	3	1	2	1	0	3	3	1	0	2	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	1	0	2	1	0	1	3	1	0	0	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
brown-spot	2	0	2	1	0	2	2	1	0	1	0	1	2	0	1	1	0	0	1	0	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	3	0	2	1	1	1	1	1	1	1	0	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	4	1	1	1	0	2	2	0	0	2	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	2	1	1	1	0	1	0	0	0	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	4	0	1	2	0	3	2	0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	5	1	2	1	1	1	3	1	1	2	1	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	3	0	1	1	0	2	0	0	0	0	1	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	4	0	2	1	1	3	1	1	1	1	0	1	2	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	3	0	2	1	1	1	2	1	1	1	1	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	4	1	1	1	0	2	3	0	0	2	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-blight	5	0	2	1	1	3	0	1	1	1	1	1	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
bacterial-pustule	3	0	1	0	1	3	3	0	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0
bacterial-pustule	1	1	1	1	0	1	0	0	1	2	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1
bacterial-pustule	2	0	1	2	1	2	1	0	0	1	0	1	2	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0
bacterial-pustule	3	1	1	1	0	3	2	0	0	2	0	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
bacterial-pustule	2	0	2	1	1	3	1	1	1	1	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0
bacterial-pustule	2	0	2	1	1	1	3	1	0	1	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0
bacterial-pustule	1	0	1	1	1	3	1	0	1	1	0	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0
bacterial-pustule	2	1	2	2	0	1	2	1	1	2	0	1	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
bacterial-pustule	5	0	1	2	1	0	1	0	0	1	1	1	1	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0
bacterial-pustule	2	1	2	0	0	1	2	1	0	2	0	1	1	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
purple-seed-stain	5	0	2	2	1	2	2	0	0	1	0	1	2	0	0	0	0	0	0	1	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	6	0	2	0	1	3	3	0	1	2	0	1	2	0	0	0	0	0	1	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	5	0	2	2	1	1	1	0	1	2	0	1	2	0	0	0	0	0	0	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	4	0	2	2	1	0	0	0	0	2	0	1	2	0	0	0	0	0	0	1	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	5	0	2	0	0	1	1	0	0	1	0	1	2	0	0	0	0	0	1	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	6	0	2	1	0	2	2	0	0	2	0	0	0	2	2	0	0	0	1	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
purple-seed-stain	3	0	2	2	0	3	3	0	0	1	0	0	0	2	2	0	0	0	0	0	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	3	0	2	0	0	3	3	0	0	2	0	0	0	2	2	0	0	0	0	0	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	5	0	2	2	0	1	1	0	1	2	0	0	0	2	2	0	0	0	0	0	0	3	0	0	0	0	0	0	0	1	0	1	0	0	0
purple-seed-stain	6	0	2	1	0	3	3	0	1	2	0	1	2	0	0	0	0	0	1	0	0	3	0	0	0	0	0	1	1	1	0	1	0	0	0
anthracnose	0	0	2	1	0	0	0	0	0	0	0	1	0	2	2	0	0	0	1	0	2	1	0	1	0	0	0	0	0	1	0	1	1	1	0
anthracnose	2	0	2	1	0	2	2	0	2	2	1	1	0	2	2	0	0	0	1	0	3	1	0	1	0	0	0	0	0	1	0	1	1	1	0
anthracnose	4	0	2	1	0	0	0	0	1	1	0	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	1	2	1	1	0	1	1	0
anthracnose	1	0	2	1	0	0	0	0	2	1	1	1	0	2	2	0	0	0	1	0	2	1	0	1	0	0	0	0	0	1	0	1	0	1	0
anthracnose	4	1	2	2	1	1	1	1	0	0	0	1	0	2	2	0	0	0	1	1	3	2	1	1	0	0	0	1	2	1	1	0	1	0	0
anthracnose	6	0	2	1	0	3	3	1	0	1	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	6	1	2	1	0	1	1	1	1	0	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	6	1	2	1	0	3	3	1	0	0	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	5	1	2	1	0	2	2	1	0	2	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	5	0	2	1	0	3	3	1	1	0	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	4	0	2	1	0	2	2	1	1	2	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	0	0	0	0	0	0
anthracnose	3	1	2	2	1	1	1	0	1	2	1	1	0	2	2	0	0	0	1	1	3	2	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	4	1	2	2	1	1	1	0	1	0	1	1	0	2	2	0	0	0	1	1	3	2	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	1	2	2	1	2	2	1	0	1	0	1	0	2	2	0	0	0	1	0	3	1	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	6	1	2	2	1	3	3	0	1	2	0	1	0	2	2	0	0	0	1	0	3	2	1	1	0	0	0	1	2	0	0	0	0	0	0
anthracnose	4	0	2	1	0	1	1	2	0	0	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	1	2	1	0	2	2	1	1	1	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	6	0	2	1	0	3	3	1	0	0	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	4	1	2	1	0	1	1	2	1	1	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	0	2	1	0	2	2	1	0	0	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	6	1	2	1	0	3	3	1	1	1	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	1	1	0	1	1	0
anthracnose	5	0	2	1	0	3	3	1	0	0	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	1	2	1	0	1	1	1	1	1	0	0	0	2	2	0	0	0	1	0	3	2	0	0	0	0	0	1	2	0	0	0	0	0	0
anthracnose	5	0	2	1	0	1	1	1	0	1	0	0	0	2	2	0	0	0	1	0	3	2	1	0	0	0	0	1	2	0	0	0	0	0	0
phyllosticta-leaf-spot	1	0	0	1	1	0	0	1	0	0	0	1	2	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	3	0	0	1	1	2	3	0	0	2	1	1	2	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	1	1	1	1	0	2	3	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	1	1	1	0	2	0	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	0	0	2	1	1	2	0	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	0	0	2	1	1	3	0	0	2	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	3	1	1	2	0	1	0	0	0	0	1	1	2	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	4	1	1	2	0	1	2	0	1	1	1	1	2	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	2	1	1	2	0	3	0	0	1	1	0	1	2	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
phyllosticta-leaf-spot	4	1	1	2	0	1	3	0	1	1	0	1	2	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	2	2	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	2	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	1	2	2	0	2	3	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	1	0	2	2	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	1	0	3	3	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	1	0	2	2	0	0	0	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	1	0	3	3	0	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	1	0	3	0	0	1	1	0	1	2	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	1	1	0	0	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	5	1	2	1	1	1	1	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	2	1	1	0	0	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	6	1	1	2	1	1	1	0	2	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	1	2	1	0	2	2	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
alternarialeaf-spot	6	0	1	1	1	0	0	0	2	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	5	0	2	1	1	2	2	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	6	0	1	2	1	3	3	0	2	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
alternarialeaf-spot	4	1	2	2	0	0	0	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	5	1	1	2	1	0	0	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	6	0	2	1	0	1	1	0	2	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	1	2	1	2	2	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
alternarialeaf-spot	4	0	2	1	0	1	0	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	1	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	1	2	2	0	3	2	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	1	3	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	0	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	1	0	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	3	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	0	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	1	1	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	3	0	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	1	0	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	3	0	2	1	0	2	1	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	1	2	1	0	3	2	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	1	3	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	2	0	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	1	2	1	0	3	1	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	4	0	2	1	0	1	2	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	3	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	0	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	1	1	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	2	2	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	1	2	2	0	3	3	0	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	1	0	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	2	1	1	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	1	2	2	0	3	2	0	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	3	3	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	3	1	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	1	2	2	0	3	2	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	5	0	2	2	0	2	1	1	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
alternarialeaf-spot	6	0	2	2	0	2	2	0	1	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	1	2	1	0	3	0	1	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	1	0	1	0	0	0	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	1	3	1	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	1	2	1	0	3	1	1	1	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	1	2	1	0	2	1	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	1	2	1	0	3	0	1	0	2	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	1	1	1	3	1	0	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	1	1	0	2	3	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	1	1	1	0	1	0	0	0	0	1	2	0	1	0	0	0	1	0	3	0	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	1	1	0	0	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	1	2	2	0	2	0	0	1	2	0	1	0	0	0	1	1	3	1	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	3	1	2	1	1	0	0	1	1	2	1	1	2	0	1	0	0	0	0	1	2	1	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	1	2	2	0	0	0	0	1	2	0	1	0	0	0	1	0	3	1	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	1	1	0	1	2	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	1	3	2	0	1	0	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	3	1	2	2	1	2	0	1	0	2	1	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	4	1	2	2	1	3	0	1	2	2	1	1	2	0	1	0	0	0	0	1	3	1	0	1	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	2	0	2	2	1	0	1	0	1	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	1	0	1	0	1	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	0	2	1	1	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	1	0	3	2	0	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	2	2	0	1	3	0	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	0	1	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	2	0	3	1	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	2	1	0	1	2	0	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	0	2	3	1	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	1	0	3	0	0	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	2	2	0	1	1	0	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	2	0	3	3	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	2	1	0	1	0	0	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	2	0	2	1	1	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	1	2	1	0	3	2	1	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	1	3	0	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	0	2	2	0	2	0	0	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	3	1	2	1	0	3	1	1	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	1	2	1	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	2	3	0	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	1	2	2	0	3	0	0	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	3	0	2	1	0	1	1	1	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	2	2	1	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	1	2	2	0	3	3	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	1	0	1	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	2	1	0	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	1	2	1	0	3	2	0	0	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	2	0	1	3	1	1	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	1	0	2	0	0	0	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	6	1	2	2	0	3	1	0	1	2	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	4	0	2	1	0	1	2	1	0	0	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
frog-eye-leaf-spot	5	0	2	2	0	2	3	0	1	1	0	1	2	0	1	0	0	0	1	0	3	2	0	1	0	0	0	1	1	0	0	0	0	0	0
diaporthe-pod-&-stem-blight	6	NA	2	2	NA	2	3	NA	NA	NA	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	6	NA	2	2	NA	1	3	NA	NA	NA	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	5	NA	2	2	NA	1	3	NA	NA	NA	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	6	NA	2	2	NA	3	3	NA	NA	NA	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	6	NA	2	2	NA	0	3	NA	NA	NA	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	1	1	1	2	NA	0	0	NA	NA	2	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	0	1	1	1	1	NA
diaporthe-pod-&-stem-blight	5	0	2	2	NA	1	3	NA	NA	0	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	5	0	2	2	NA	3	3	NA	NA	0	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
diaporthe-pod-&-stem-blight	6	0	2	2	NA	3	3	NA	NA	1	0	0	NA	NA	NA	NA	NA	NA	1	NA	0	0	1	0	0	0	0	1	2	1	1	1	1	1	NA
cyst-nematode	2	NA	NA	NA	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	3	NA	NA	NA	NA	2	2	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	4	NA	NA	NA	NA	1	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	2	NA	NA	NA	NA	2	2	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	3	NA	NA	NA	NA	2	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	4	NA	NA	NA	NA	3	2	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	4	NA	NA	NA	NA	3	1	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
cyst-nematode	3	NA	NA	NA	NA	3	2	NA	NA	NA	1	1	NA	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	NA	NA	NA	NA	2	NA	1	0	NA	1	NA	2
2-4-d-injury	5	NA	NA	NA	NA	NA	1	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	0	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	1	NA	NA	NA	NA	NA	1	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	2	NA	NA	NA	NA	NA	2	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	3	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	4	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	6	NA	NA	NA	NA	NA	2	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	0	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	1	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	2	NA	NA	NA	NA	NA	1	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	3	NA	NA	NA	NA	NA	2	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	4	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	5	NA	NA	NA	NA	NA	0	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	6	NA	NA	NA	NA	NA	1	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2-4-d-injury	0	NA	NA	NA	NA	NA	2	NA	NA	NA	NA	1	0	2	2	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
herbicide-injury	0	1	NA	0	NA	0	0	NA	NA	NA	1	1	0	2	2	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1
herbicide-injury	2	1	NA	0	NA	0	0	NA	NA	NA	1	1	0	2	2	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1
herbicide-injury	0	1	NA	0	NA	1	3	NA	NA	NA	1	1	2	1	1	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1
herbicide-injury	2	1	NA	0	NA	1	3	NA	NA	NA	1	1	2	1	1	0	1	NA	1	NA	NA	NA	NA	NA	NA	NA	NA	3	NA	NA	NA	NA	NA	NA	1

1. Investigate the frequency distributions for the categorical predictors. Are any of the distributions degenerate in the ways discussed earlier in this chapter?

Frequency Distribution Histogram Plots

Soybean %>% 
  gather(variable, value) %>%
  ggplot(., aes(value)) + 
  geom_histogram(alpha=0.5, stat = "count", color="dodgerblue4", fill = "dodgerblue4") +
  facet_wrap(~variable, scales ="free", ncol = 4) +
  labs(x = element_blank(), y = element_blank()) +
  ggtitle("Frequency Distribution Plot for Class variable + Categorical Predictor")

## Warning: attributes are not identical across measure variables;
## they will be dropped

## Warning: Ignoring unknown parameters: binwidth, bins, pad

Based on the frequency distribution histogram plot above, it appears that there are few categorical predictors demonstrating degenrate distribution. These predictors include -

• leaf.malf
• leaf.shread
• leaves
• lodging
• mold.growth
• mycelium
• sclerotia

Identifying Near Zero Variance Predictors

The caret package function nearZeroVar() will return the column numbers of any predictors that fulfill the conditions of degenrate distributions -

## Determine a predictor set without highly sparse and unbalanced distributions:
isNZV <- nearZeroVar(Soybean)
colnames(Soybean)[isNZV]

## [1] "leaf.mild" "mycelium"  "sclerotia"

NZV predictors as identified above can be safely removed from the model toward better prediction and model simplification.

2. Roughly 18% of the data are missing. Are there particular predictors that are more likely to be missing? Is the pattern of missing data related to the classes?

Missing Value Analysis

Below is an analysis of predictors with NA values.

## Counts of missing data per feature
train_na_df <- data.frame(apply(Soybean, 2, function(x) length(which(is.na(x)))))
train_na_df1 <- data.frame(apply(Soybean, 2,function(x) {sum(is.na(x)) / length(x) * 100}))

train_na_df <- cbind(Feature = rownames(train_na_df), train_na_df, train_na_df1)
colnames(train_na_df) <- c('Feature Name','No. of NA Recocrds','Percentage of NA Records')
rownames(train_na_df) <- NULL


train_na_df%>% filter(`No. of NA Recocrds` != 0) %>% arrange(desc(`No. of NA Recocrds`)) %>% kable() %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width="100%",height="300px")

Feature Name	No. of NA Recocrds	Percentage of NA Records
hail	121	17.7159590
sever	121	17.7159590
seed.tmt	121	17.7159590
lodging	121	17.7159590
germ	112	16.3982430
leaf.mild	108	15.8125915
fruiting.bodies	106	15.5197657
fruit.spots	106	15.5197657
seed.discolor	106	15.5197657
shriveling	106	15.5197657
leaf.shread	100	14.6412884
seed	92	13.4699854
mold.growth	92	13.4699854
seed.size	92	13.4699854
leaf.halo	84	12.2986823
leaf.marg	84	12.2986823
leaf.size	84	12.2986823
leaf.malf	84	12.2986823
fruit.pods	84	12.2986823
precip	38	5.5636896
stem.cankers	38	5.5636896
canker.lesion	38	5.5636896
ext.decay	38	5.5636896
mycelium	38	5.5636896
int.discolor	38	5.5636896
sclerotia	38	5.5636896
plant.stand	36	5.2708638
roots	31	4.5387994
temp	30	4.3923865
crop.hist	16	2.3426061
plant.growth	16	2.3426061
stem	16	2.3426061
date	1	0.1464129
area.dam	1	0.1464129

Based on above analysis, it looks like there are certain predictors which are more likely to be missing. Some of these most missing value predictors are - hail, sever, seed.tmt, lodging, germ etc.

gg_miss_fct(x = Soybean, fct = Class) + labs(title = "NA in Soybean Predictors and Disease Class")

From the above plot, there seems to be a pattern of missing data related to classses. For example, out of 19 prediction classes, only 5 classes - 2-4-d-injury, cyst-nematode, herbiside-injury, diaporthe-pod-&-stem-blight & phytopthora-rot seem to have predictors with missing values.

3. Develop a strategy for handling missing data, either by eliminating predictors or imputation.

In order to optimize the prediction model, we need to re-evaluate the list of predictors that need to be part of the model and also handle the missing values by deploying appropriate imputation techniques.

Removing Near Zero Variance Predictors

It can also be noted that the 3 Near Zero Variance predictors ("leaf.mild", "mycelium" & "sclerotia") identified in the first part of the problem also showed up in the list of missing value predictors. So, as a first step these predictors can be removed from the data set.

SoybeanTrans <- Soybean %>% select(-c("leaf.mild", "mycelium","sclerotia"))

Imputation Strategy:

For imputation of the missing data for the categorical predictors, various methods can be adopted. Below are some of the strategies that can be followed -

• Ignore observation which can cause loosing out on some of the information present in original data. This method typically works well for large data sets.
• Replace by most frequent value. This is simplistic approach but may not yield the best possible result.
• Replace using an algorithm like KNN using the neighbours. The advantage of this approach is that the imputed data are confined to be within the range of the training set values. The disadvantage of this approach is that the entire training set is required every time a missing value needs to be imputed. But in general, nearest neighbor approach is fairly robust to the tuning parameters, as well as the amount of missing data.
• Leveraging the training data set, predict the observation using a multiclass predictor.

K Nearest Neighbor Technique

I have adopted the KNN approach for imputation for the current categorical data set using the knnImputation() fruntion from DMwR package. I am using k=10 as a tuning parameter.

#data <- prodNA(SoybeanTrans, noNA = 0.2)
SoybeanImputed <- knnImputation(SoybeanTrans, k = 10)

gg_miss_fct(x = SoybeanImputed, fct = Class) + labs(title = "NA in Soybean Imputed Predictors and Disease Class")

From the above plot, we can conform that post imputation, all the missing values have been imputed using KNN.

Also, we need to check the colinearity amongst the predictors to identify if there is additional scope to remove predictors further from the data set.

Correlation Plot

SoybeanImputed <- SoybeanImputed %>% select(-c("Class"))

indx <- sapply(SoybeanImputed, is.factor)
SoybeanImputed[indx] <- lapply(SoybeanImputed[indx], function(x) as.numeric(as.character(x)))

corrMatrix <- round(cor(SoybeanImputed),4)

corrMatrix %>% corrplot(., method = "color", outline = T, addgrid.col = "darkgray", order="hclust", addrect = 4, rect.col = "black", rect.lwd = 5,cl.pos = "b", tl.col = "indianred4", tl.cex = 1.0, cl.cex = 1.0, addCoef.col = "white", number.digits = 2, number.cex = 0.8, col = colorRampPalette(c("darkred","white","dodgerblue4"))(100))

Based on the above Corrplot, further anlysis can be done to identify colinearity amongst predictors and a decision can be made to remove more predictors from the model.