Wheat Water Stress detection using chlorophyll fluorescence image processing

Published: 29-06-2020| Version 3 | DOI: 10.17632/ybjs4ppyzf.3
ankita gupta,
Gurmeet kaur,
Lakhwinder kaur


The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images every day (Control and Drought) have been captured for a period of sixty days. A total of (1140 x 2) images are in used for this research work. Dataset is generated after applying hybridised segmentation algorithms to increase the water stress detection efficiency. 23 GLCM Texture features are identified from the dataset listed following: 1. Autocorrelation: (out.autoc) 2. Contrast: matlab (out.contr) 3. Correlation: matlab (out.corrm) 4.Correlation: (out.corrp) 5.Cluster Prominence: (out.cprom) 6. Cluster Shade: (out.cshad) 7.Dissimilarity: (out.dissi) 8. Energy: matlab (out.energ) 9. Entropy: (out.entro) 10. Homogeneity: matlab (out.homom) 11. Homogeneity: (out.homop) 12. Maximum probability: (out.maxpr) 13. Sum of sqaures: Variance (out.sosvh) 14. Sum average (out.savgh) 15. Sum variance (out.svarh) 16. Sum entropy (out.senth) 17. Difference variance (out.dvarh) 18. Difference entropy (out.denth) 19. Information measure of correlation1 (out.inf1h) 20. Informaiton measure of correlation2 (out.inf2h) 21. Inverse difference (INV) is homom (out.homom) 22. Inverse difference normalized (INN) (out.indnc) 23. 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


Steps to reproduce

This dataset has been created with the help of MATLAB GLCM library.https://in.mathworks.com/help/images/ref/graycomatrix.html