Image-based blight disease detection in crops using ensemble deep neural networks for agricultural applications

Abstract

Blight disease poses a significant threat to agricultural output that results in large crop losses worldwide. Plant diseases must be promptly identified and managed to maintain crop health and maximise yields. This research presents a novel ensemble-based deep-learning model for plant blight disease detection, especially for agricultural applications. The suggested model uses convolutional neural networks (CNNs) for image recognition to accurately and automatically detect blight-affected areas in plant leaf images. An extensive dataset of plant leaf images was gathered to train and evaluate the model, including samples from both healthy and diseased plants. This ensemble-based deep learning model outperformed conventional deep learning and machine learning models in extracting characteristics that differentiated between plants affected by blight and those that weren’t. The proposed model (ResNet11) is a dependable and effective tool for on-the-spot disease detection in the field of potato, tomato and pepper, as demonstrated by experimental results that illustrate an accuracy of over 99 % for potato and pepper crops as a 3-class and 2-class problem respectively. Moreover, we get an accuracy of over 87 % for tomato plants as a 10-class problem.