Classification of Beans Leaf Diseases using Fine Tuned CNN Model

Abstract

Automation in the agricultural field is a requirement of all the countries. Usually, plant diseases are observed in the form of visual symptoms and many deep learning-based models have achieved outstanding results in the classification of plant leaf diseases in recent years. Beans plant diseases like bean rust disease and angular leaf spot disease reduce the bean crop yield. To treat the problem at an early stage, an appropriate diagnosis for this crop disease is required. In this paper, three deep learning-based pre-trained models namely MobileNetV2, EfficientNetB6, and NasNet were used to perform transfer learning on the Beans Leaf image dataset containing 1295 images with three different classes. Furthermore, different optimization techniques were also used to highlight the variation in performance of different Convolutional Neural Network (CNN) models. The analysis of experimental results shows that EfficientNetB6 performs better with 91.74% accuracy than other models. This study would be helpful to understand the role of different optimizers on the CNN models. Furthermore, agricultural scientists could employ a real-time-based application of the best-suited model for the farmers to adopt prevention measures in disease-vulnerable areas. As a result, prompt action would aid in minimizing plant productivity loss. It will further revenue the economic growth and agricultural productivity.