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

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. • The proposed model has less training dataset than testing dataset because the use of dropout layer and batch normalization; the advantage of these two layers are some data will be relaxed and not go to the training set. • Logically this concept refers to more comparison of healthy leaf dataset with affected leaf dataset and result gives that more accuracy. • Another essential feature of our model is that the smaller image size i.e. 128x128 pixels caused the model to be faster than the previous model i.e. in where image size is used 256x256 pixels and in image size is used 224x224 pixels. • The early identification and classification of potato, tomato and pepper leaf disease. There are some factors, such as land temperature, soil properties, and leaf area of plants, were not together considered in the previous paper. • The proposed work focus on how much area of the leaf of the plant is affected by early blight, late blight and other diseases.