Genetic Algorithm–Aided Deep Feature Selection for Improved Rice Disease Classification

Pests and diseases pose significant threats to crop safety and accessibility. Deep learning integration with traditional pest management fosters sustainable agriculture, minimizes chemical pesticide use, and facilitates early detection of pests and crop diseases through image and sensor data analysis. To ensure food security, reduce costs, and enhance overall production, computer vision techniques are essential for processing complex, high-dimensional real-world data. However, data dimensionality reduction is crucial for achieving accurate disease identification. Pre-trained models can extract valuable features from data. In this study, InceptionV3 and MobileNet V2 were utilized to extract comprehensive features from a small UCI rice leaf disease dataset. However, classifier performance using these features is uncertain due to factors such as domain mismatch, high-level data representation, model biases, dataset variability, and task complexity. To address these challenges, a rigorous evaluation of pre-trained feature suitability is necessary. A genetic algorithm–based feature selection (FS) approach was employed. FS streamlines data, reducing the information required for disease identification. An optimized ANN classifier, MobileNet-GA-ANN, achieved a 96.58% accuracy, outperforming other methods.