A Review of Crop Pest Classification and Pesticide Recommendations Using AI Techniques

This project outlines the development of an automated system for crop pest classification and subsequent pesticide recommendation, addressing critical challenges in modern agricultural pest management. The initiative leverages Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) and Deep Learning (DL) models, to significantly improve the speed and precision of pest identification. The system is designed to process visual and potentially sensor-based data, such as images captured from crop fields, to accurately detect and classify diverse pest species. Following the identification of specific pests, the system will integrate a data-driven module to generate informed recommendations for appropriate pesticide application. This methodology aims to optimize pesticide usage, thereby contributing to more sustainable

agricultural practices and reducing reliance on conventional, often labor-intensive, human-dependent detection and control methods. The implementation involves the selection, training, and deployment of robust ML/DL architectures for image-based classification, coupled with the integration of a decision-support framework for pesticide guidance. The overarching goal is to provide a practical, efficient, and ethical tool to support effective pest management strategies in contemporary agriculture.

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