Wheat Water Stress detection using chlorophyll fluorescence image processing

The data consist of a file system-based data of Raj 3765 variety of wheat. There are twenty-four chlorophyll fluorescence images (Control and Drought) that have been captured for a period of sixty days. A total of (1440 x 2) images are used for this research work.

The generated chlorophyll fluorescence set is subjected to various pre-processing operations including noise removal, contrast enhancement. Pre-processed images are then segmented using novel segmentation approach “C-fit Kmeans” segmentation to increase water stress detection accuracy for automation procedure. 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 undergo various statistical processes viz. correlation, factor, and Clutsring analyzing to identify the key drought detection variables suited best for water stress which in-turn help to build (AIM) Agronomical Inferencing Model for the wheat crop to understand the behavioral change in texture variables in the presence of stress.

The dataset has been produced using MATLAB GLCM libraries https://in.mathworks.com/help/images/ref/graycomatrix.html