Optimization of Plant Disease Detection and Classification Using an Antlion-Optimized VGG16 Model with Fuzzy Rough C-Means Segmentation

Abstract— The agricultural sector in India, supporting over 65% of the population, faces significant challenges from plant diseases that threaten crop productivity and food security. Traditional disease identification methods are often slow and require expert knowledge. This paper proposes a novel, automated framework for accurate plant disease detection by integrating advanced image processing with deep learning. The methodology employs a Median filter for image pre-processing, the Fuzzy Rough C-Means (FRCM) clustering algorithm for robust segmentation of diseased leaf regions, and a Convolutional Neural Network (CNN) for classification. The core innovation lies in enhancing a standard VGG16 CNN architecture using the Antlion Optimization (ALO) algorithm to optimize its hyperparameters, specifically the number of neurons in a fully connected layer, thereby improving feature learning and classification performance. Trained and tested on a dataset of cotton leaf images encompassing healthy samples and four disease types, the proposed ALO-enhanced VGG16 model achieved a high average classification accuracy of 93.33%. This performance surpasses that of other standard classifiers, including basic CNN, SVM, and ResNet models. The findings demonstrate that the integration of metaheuristic optimization with deep learning offers a powerful, scalable tool for precise plant disease diagnosis, with the potential to aid sustainable agricultural practices.