Detection of Diseases in Blackgram (Vigna mungo L.) Using Machine Learning Models: A Case Study

Abstract

Black gram (Vigna mungo L.) is widely used in Indian cuisine and one of the most significant pulses cultivated in India. Identification of plant diseases at their earlier stages is essential to take necessary plant protection measures to reduce yield loss to the farmers. Anthracnose and Powdery Mildew are the major diseases in black gram which causes significant yield losses to the farmers. In this research study, advanced disease detection machine learning models such as Multinomial Logistic Regression, Random Forest Classifier were employed to assist the farmers in detection of plant leaf diseases in blackgram at their early stages of growth. For this present study, Image data sets were collected from Thanjavur block, Thanjavur district, Tamil Nadu. Results of the study showed that accuracy of Random Forest Classifier was higher with train accuracy 99.17% and test accuracy 97.00% when compared to the other machine learning methods for detection of plant leaf diseases in black gram, which aids in promotion of smart agriculture.