Transfer Learning Based Detection and Classification of Diseases in Groundnut Leaves

Abstract

One of the most important economic activities in the global agricultural sector, groundnut production is threatened by diseases involving leaves including; Leaf Spot, Alternaria Leaf Spot, Rust, and Rosette. Old approaches to disease diagnosis are costly, cumbersome, and slow, especially in intensive farming businesses. This work examines the use of transfer learning in automating and improving the detection and classification of groundnut leaf diseases. To address the class imbalance and augment the robustness of the model, men and women from different ethnic backgrounds with a total of 1,720 images in five categories were used to apply rescaling, rotation, and horizontal flipping. The following deep learning base models: VGG19, InceptionV3, MobileNetV2, Exception, and DenseNet201 models were fine-tuned and tested according to the accuracy, precision, recall, and F1 score. Out of all the neural networks, Dense Net201 was the best since it got the best test accuracy of 97.50%. The developed system offers a versatile, real-time disease diagnosis solution for farmers to enhance their farming decisions, limiting pesticide application, and increasing agricultural yield. This work contributes to both the development of Al-assisted agricultural applications and the discussion of transfer learning for solving practical problems.