AI-Driven Pest Detection and Management Systems for Sustainable Agriculture in Developing Economies

Abstract

The increasing impact of insect pests on crop productivity poses a significant challenge to sustainable agriculture, particularly in developing economies where food security and farmer livelihoods are highly vulnerable. This study investigates the application of AI-driven pest detection and management systems using deep learning–based computer vision models to enable precise, efficient, and environmentally sustainable pest control. An experimental mixed-method approach was adopted, integrating quantitative performance evaluation of AI models with qualitative field-level validation. High-resolution image data collected through ground-based cameras and sensor-enabled platforms were used to train and evaluate deep learning architectures for pest identification and severity assessment. The results demonstrate that the proposed AI-driven framework achieves consistently high detection accuracy, precision, recall, and F1-scores across diverse environmental conditions and pest densities. Comparative analysis indicates a substantial reduction in false detections and unnecessary pesticide application when compared with conventional manual scouting practices. Furthermore, AI-assisted decision support enabled targeted interventions, contributing to improved crop protection efficiency and reduced environmental impact. The scalability and robustness of the system across heterogeneous datasets highlight its suitability for deployment in resource-constrained agricultural settings. Overall, the findings confirm that AI-driven pest management systems can significantly enhance agricultural productivity while promoting sustainable practices. This research provides empirical evidence supporting the adoption of deep learning–based pest detection technologies as a viable solution for addressing food security challenges and advancing sustainable agriculture in developing economies.