Advancing Black Gram (Vigna Mungo L.) Disease Diagnosis Through Deep Learning Techniques

Abstract

Interestingly in sustainable crop protection disease diagnosis and management are crucial in sustainable crop production It plays a captious role in rain-fed pulses because the occurrence of season of cropping cultivation after main crop availability of soil moisture in poor conditions consecutively following the same cultivars are acting a predominant role in disease diagnosis approaches and confirmation Under these situations occurrence of the manual errors or mis find faults resulting in complete drawbacks to disease diagnosis and management for farmers and scientists worldwide Keeping this background applying deep learning techniques is most helpful in diagnosing plant diseases silently and superiorly Deep learning techniques were carried out in this study to diagnose foliar diseases in black gram such as anthracnose leaf crinkle powdery mildew and yellow mosaic that causes a severe yield loss 50 silently accompanied by green biomass A vast field survey was conducted in the black gram growing Cauvery delta zone of four blocks in Pudukkottai district Tamil Nadu India with 27376 images collected Furthermore the advanced inception V3 model has been used for analysis assessment and prediction for the diagnosis of diseases The model was investigated with 20 percent 40 percent and 50 percent dropout rates The result showed that an Inception V3 model with a 20 percent dropout rate gave the best performance with an accuracy of 99 22 percent and a loss of 0 0249 The high performance rate shows automated disease diagnosis which helps the farmers develop disease management strategies at the preliminary stages of their growth