A Novel Hybrid Approach Based on CNN for Corn Diseases Detection

Abstract

Corn is one of the most economically important crops globally, significantly improving food security and agricultural productivity. However, corn plants face various foliar diseases, which can significantly reduce crop productivity. Accurate and early detection of corn diseases is an imperative task for maintaining crop health and ensuring food security. In this study, we propose a novel approach for corn disease detection by integrating DenseNet121, a powerful convolutional neural network (CNN) architecture, with a deep neural network (DNN) classifier. This hybrid model, called DenseNetDNN, combines the feature extraction capabilities of DenseNet121 with the classification capabilities of a DNN, aiming to enhance disease detection accuracy. The proposed model’s performance is compared against four widely used pre-trained CNN models: ResNet50, MobileNet, EfficientNetB0, and Xception. All models are evaluated using accuracy, precision, and recall. Additionally, the study employs GradeCam, an advanced grading system, to automate and standardize the performance evaluation of the proposed model. Results demonstrate that the DenseNetDNN model outperformed all other models in terms of identifying corn diseases; it achieves superior performance with an accuracy of 96.1%, precession of 0.952, and recall of 0.958. which demonstrates the efficiency of DenseNetDNN in advancing agricultural disease detection. This research contributes to the development of automated solutions for agricultural monitoring, with implications for improving crop management practices and ensuring global food security.