Waterstress
OpenML dataset with id 42464
No author found.
Full work available at URL: https://api.openml.org/data/v1/download/21854347/Waterstress.arff
Upload date: 30 May 2020
Copyright license: CC0
Dataset Characteristics
Number of classes: 0
Number of features: 23 (numeric: 23, symbolic: 0 and in total binary: 0 )
Number of instances: 1,188
Number of instances with missing values: 0
Number of missing values: 0
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
This page was built for dataset: Waterstress
