Heuristic Optimization with Deep Learning based Maize Leaf Disease Detection Model

Abstract

Maize is a main global food crop and is the most productive grain crop. It is also an optimum feed for the progress of animal husbandry and crucial raw material for the chemical industry, light industry, health medicine, and. Diseases are the significant factor limiting the high and stable yield of maize. For classifying diseases based on that damages the plants, the leaves of affected plants can be studied utilizing pixel-wise approaches. The Convolutional Neural Network (CNN) is the most effectual Deep Learning (DL) algorithm utilized in classification of an image to correctly diagnose plant ailments. Therefore, this study introduces an automated Maize Leaf Disease Detection using Biogeography-based Optimization with Deep Learning (MLDDBBODL) algorithm. The presented MLDD-BBODL method aims to identify and classify the occurrence of maize disease accurately. To achieve this, the presented MLDD-BBODL method employs contrast enhancement as an initial preprocessing stage. Besides, the SqueezeNet model is exploited for the derivation of feature vectors. Meanwhile, a Backpropagation Neural Network (BPNN) classifier is utilized for the recognition of maize leaf ailments. Furthermore, the BBO technique is implemented for the parameter tuning of the BPNN model which in turn enhances the classification results. The performance evaluation of the MLDD-BBODL technique is carried out on the leaf disease dataset. An extensive comparison study stated that the MLDD-BBODL technique reaches outperformed results over other recent approaches in terms of different measures.