Deep Learning and Grey Wolf Optimization Technique for Plant Disease Detection: A Novel Methodology for Improved Agricultural Health

Plant disease outbreaks have a profound impact on the agricultural sector, leading to substantial economic implications, compromised crop yields and quality, and potential food scarcity. Consequently, the development of effective disease prevention and management strategies is crucial. This study introduces a novel methodology employing deep learning for the identification and diagnosis of plant diseases, with a focus on mitigating the associated detrimental effects. In this investigation, Convolutional Neural Networks (CNNs) were utilized to devise a disease identification method applicable to three types of plant leaves - peppers (two classes), potato (three classes), and tomato (nine classes). Preprocessing techniques, including image resizing and data augmentation, were adopted to facilitate the analysis. Additionally, three distinct feature extraction methods - Haralick feature, Histogram of Gradient (HOG), and Local Binary Patterns (LBP) - were implemented. The Grey Wolf Optimization (GWO) technique was employed as a feature selection strategy to identify the most advantageous features. This approach diverges from traditional methodologies that solely rely on CNNs for feature extraction, instead extracting features from the dataset through multiple extractors and passing them to the GWO for selection, followed by CNN classification. The proposed method demonstrated high efficiency, with classification accuracies reaching up to 99.8% for pepper, 99.9% for potato, and 95.7% for tomato. This study thus provides a progressive shift in plant disease detection, offering promising potential for improving agricultural health management. In conclusion, the integration of deep learning and the Grey Wolf Optimization technique presents a compelling approach for plant disease detection, demonstrating high accuracy and efficiency. This research contributes a significant advancement