Tomato Leaf Disease Prediction Using Transfer Learning

Tomato leaf is one of the essential edible food item which are affected by diseases. The advent of machine learning and deep learning methods has made crop diseases easier to recognize. The Deep Learning (DL) has emerged as a powerful technique in recent years for image processing and data analysis, with promising results. DL has been applied in various domains, including the field of agriculture. In deep learning, Convolution Neural Network (CNN) is one of the architectures are widely used. Transfer learning is a new approach in DL where the pre-trained models are used to train a new dataset for expediting the training process. This research work focuses on developing a Transfer Learning driven Prediction model for leaf disease detection. In this paper, a new composite and comprehensive prediction schemes specifically for tomato leaf disease analysis are developed. This Deep Neural Network (DNN) classifier is designed to build on the combination of two deep learning models, namely VGG16 and VGG19. The results were recorded in terms of Precision, Recall, F1-score and accuracy. The improved accuracy of Transfer Learning experimented in this reported research work vouches for its application in the prediction of Leaf Disease.