Implementation of Ant Colony Optimization and Principal Component Analysis for Early Prediction of Tomato Leaf Diseases: A Feature Reduction Approach

Abstract

This paper details the implementation of a sophisticated algorithmic framework designed for the early prediction of various tomato leaf diseases, crucial for enhancing crop management and yield. The research deploys image processing techniques to extract pivotal features such as Red Mean, Green Mean, Blue Mean, Height, Width, and Defect Color channels from high-resolution images of tomato leaves. These features serve as indicators for diseases including Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, and infestations by Two-spotted Spider Mites. The core of the implementation lies in the integration of Ant Colony Optimization (ACO) with Principal Component Analysis (PCA) for feature reduction, which streamlines the dataset while retaining critical information. This combination not only reduces computational load but also improves the accuracy of the early prediction model. The paper demonstrates the application of this hybrid approach and compares its performance with existing models, emphasizing its efficiency and accuracy in early-stage disease prediction. The findings indicate that the proposed method outperforms traditional techniques, offering a reliable and scalable solution for agricultural disease management.