Adaptive Disease Detection Algorithm Using Hybrid CNN Model for Plant Leaves

Abstract

Plant diseases can harm crops and reduce the amount of food that can be cultivated, which is problematic for farmers. Technology is being utilized to develop computer-based programs that can recognize plant diseases and assist farmers in making better decisions after identifying plant leaf diseases. In most of these models, machine learning algorithms are applied, to make predictions about potential plant diseases using mathematical models and neural networks. Many researchers discussed the variants of DNN and CNN algorithms to solve the discussed problems and gave better results. In this paper, the novel approach is discussed and implemented where the plant disease is identified whether the plant leaf captured image has a noisy background or not; or whether the leaf image is segmented or not. The authors developed an adaptive algorithm which gives the results in two phases: the classification of the plant disease based on the original input leaf image and secondly, the identification of plant leaf disease after applying the segmentation process. The result of this two-phase proposed model is analyzed and compared with existing popular models like AlexNet, ResNet-50, and the EffNet the results are convincing. The proposed model has 97.39% accuracy when the noiseless image is taken; while the 90.26% accuracy is there, in case of noisy background image as an input; and the results are outstanding, if the authors are applying their segmentation-based AH-CNN model on the noisy real-time image, the accuracy is 95.27%.