Data
Waterstress

Waterstress

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Author: Ankita Gupta, Dr.Lakwinder Kaur, Dr. Gurmeet Kaur Source: Unknown - 01-11-2019 Please cite: Water stress dataset for Indian variety of wheat crop: The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images captured every alternative day (Control and Drought) that have been captured for a period of sixty days. A total of (594 x 2) images are used for this research work. This dataset comprises of images of wheat crop using Chlorophyll Fluorescence modality. Which is further used to identify drought water stress at canopy level in the wheat crop with the help of Image Processing algorithms. Autocorrelation: (out.autoc) Contrast: matlab (out.contr) Correlation: matlab (out.corrm) 4.Correlation: (out.corrp) 5.Cluster Prominence: (out.cprom) Cluster Shade: (out.cshad) 7.Dissimilarity: (out.dissi) Energy: matlab (out.energ) Entropy: (out.entro) Homogeneity: matlab (out.homom) Homogeneity: (out.homop) Maximum probability: (out.maxpr) Sum of sqaures: Variance (out.sosvh) Sum average (out.savgh) Sum variance (out.svarh) Sum entropy (out.senth) Difference variance (out.dvarh) Difference entropy (out.denth) Information measure of correlation1 (out.inf1h) Informaiton measure of correlation2 (out.inf2h) Inverse difference (INV) is homom (out.homom) Inverse difference normalized (INN) (out.indnc) Inverse difference moment normalized (out.idmnc) These variables then undergone through various statistical processes to identify the key detection variables suited best for water stress which in-turn help to build root cause analysis model (RCA) for water stress. The dataset has been produced using MATLAB GLCM libraries https://in.mathworks.com/help/images/ref/graycomatrix.html Texture feature analysis is done using 23 texture GLCM features to extract features pertaining to water stress identification. These variables then undergone through various statistical processes to identify the key detection variables suited best for water stress which in-turn help to build root cause analysis model (RCA) for water stress. The dataset has been produced using MATLAB GLCM libraries https://in.mathworks.com/help/images/ref/graycomatrix.html

23 features

class (target)numeric2 unique values
0 missing
autocnumeric1187 unique values
0 missing
contrnumeric1187 unique values
0 missing
corrmnumeric1178 unique values
0 missing
corrpnumeric1178 unique values
0 missing
cpromnumeric1187 unique values
0 missing
cshadnumeric1187 unique values
0 missing
dissinumeric1186 unique values
0 missing
energnumeric1181 unique values
0 missing
entronumeric1185 unique values
0 missing
homom1numeric1177 unique values
0 missing
homopnumeric1180 unique values
0 missing
maxprnumeric1179 unique values
0 missing
sosvhnumeric1187 unique values
0 missing
savghnumeric1187 unique values
0 missing
svarhnumeric1187 unique values
0 missing
senthnumeric1186 unique values
0 missing
dvarhnumeric1187 unique values
0 missing
denthnumeric1185 unique values
0 missing
inf1hnumeric1182 unique values
0 missing
inf2hnumeric1187 unique values
0 missing
homomnumeric1147 unique values
0 missing
indncnumeric1097 unique values
0 missing

19 properties

1188
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
23
Number of numeric attributes.
0
Number of nominal attributes.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
1
Average class difference between consecutive instances.
100
Percentage of numeric attributes.
0.02
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.

7 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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