Comparative Study on Identification and Classification of Plant Diseases with the Support of Transfer Learning

Abstract

Crop growth and yield are essential aspects that influence the field of agriculture as well as the farmer economically, socially, and in every possible way. So, it is necessary to have close monitoring at various stages of crop growth to identify the diseases at the right time. But, the human naked eye may not be sufficient and sometimes misleading scenarios arise. In this aspect, automatic recognition and classification of various diseases of a specific crop are necessary for accurate identification. This thought gave inspiration for the present proposed framework. The proposed framework mainly concentrated on the transfer learning phenomenon based on three different pre-trained models such as VGG-16, ResNet-50, and ResNet-50 v2, and then compared the three models based on transfer learning models based on various standard evaluation metrics. VGG-16-based transfer learning model achieved an accuracy of 98.74%, ResNet-50-based transfer learning model achieved an accuracy of 98.84%, and ResNet-50 v2-based transfer learning model achieved an accuracy of 98.21%. The dataset considered for the implementation is the “PlantVillage” dataset which includes the various diseased and healthy leaves of Pepper, Potato, and Tomato, and it is an openly available dataset through Kaggle.