Detection of damage caused by chickpea anthracnose (Ascochyta rabiei (pass) labr.) disease on leaves and capsule using image processing techniques

Although our country has an important place in the world chickpea production, it is always faced with anthracnose disease, which occurs in chickpea and leads to the destruction of almost the entire crop if no measures are taken. In this study, it was aimed to determine the damage caused by chickpea anthracnose disease on leaves and capsules by using image processing methods. In the study, images were taken from two different fields at different development levels of the disease. Fifty capsule and 50 leaf images at different developmental levels of the disease were processed by image processing technique using the Image Processing Toolbox module in MATLAB program. In the study, k-means clustering algorithm and Otsu's thresholding method were used. The image processing method predicted the disease levels with an RMSE of 3.18 and Theil UII of 0.0596. According to the results obtained, the damage severity of chickpea anthracnose disease in the capsule and leaf was successfully determined with the developed image processing method.