Optimizing Deep Learning for Cotton Leaf Disease Detection Using Meta-Heuristic Feature Selection Algorithms

Effective and efficient disease detection is crucial, particularly for economically important crops like cotton. In this paper, we move from the initial development of a deep-learning model for cotton leaf disease detection, called Deep-CCNet, to a more comprehensive comparison of different feature selection algorithms, such as RainWater Algorithm, Particle Swarm Optimization, Bee Evolutionary Algorithm, Genetic Algorithm, and Binary Dragonfly Algorithm. Although Deep-CCNet achieved satisfactory classification performance, the goal of this study is to improve the classification performance and efficiency of deep learning models with meta-heuristic feature selection techniques. This study aims to determine which feature selection method achieves the best balance between performance and computational efficiency. We used the Kaggle “cotton leaf disease dataset”, which has 1,711 images from four classes (namely curl virus, bacterial blight, fusarium wilt, and healthy leaf images), to compare these techniques systematically. Our research attempts to find the most effective method that maximizes model performance while minimizing computing resources, in addition to benchmarking the computational and performance parameters of each approach. The results of this study provide a new approach for the choice of feature selection methods in plant pathology, leading to better early disease diagnosis and increased crop resilience via efficient farming practices.