Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization

Abstract

India is one of the leading countries in Tomato cultivation and production. Coimbatore, Dharmapuri, Salem,Krishnagiri are some of the foremost districts in tomato production. According to a survey, Tomato Leaf Curl virus is one of the most ravage class of disease and the maximum incidence of the disease is reported as 95.3% in Dharmapuri district of Tamil Nadu. The objective of the work is to identify the most recurrent tomato plant diseases using deep learning-based image classification techniques and to develop a plant disease classification web application. In this paper, Tomato based leaf disease images alone is used from Plant Village Dataset downloaded from Kaggle platform. Inception V3, a pre-trained transfer learning model is used to classify this multi-class tomato plant disease by using the leaf symptoms such as dark brown lesions, concentric rings etc. Data augmentation is performed while training the model on the images using Keras Image Data Generator on various layers and feature extraction is performed. The training accuracy and validation accuracy of the model before and after fine-tuning is 80.21%,76.29% and 81.72%,77.63% respectively. The highest precision, recall and F1 score is 95, 93 and 94 respectively is recorded for Tomato Yellow Leaf Curl Virus Disease for which the yield losses is severe. Further, activations are used to generate an attention map in the form of Heat Maps which is included as a post processing step before the classification of the output. Plant Leaf Disease Classification- A web application is deployed using Streamlit Python library and Ngrok services