Image Classification For Plant Disease Prediction Using Ensemble Deep Transfer Learning

Abstract

Plant diseases continue to pose a significant threat to global agriculture. Plant diseases have a serious impact on crop production and food security. Early and accurate detection of these diseases is crucial for minimizing yield losses and ensuring food security. In this study, we explore the efficacy of transfer learning, a powerful technique in deep learning, for plant disease prediction through image classification. We show that transfer learning can significantly improve the accuracy as well as efficiency of disease classification by using pre-trained convolutional neural network architectures and fine-tuning them on a variety of plant image datasets. Our experimental results show that the effectiveness of transfer learning in accurately classifying various plant diseases, thereby offering a promising solution for timely disease detection.