Artificial Intelligence and Machine Learning in Pest and Weed Management for Sustainable Agriculture

Abstract

Sustainable agriculture is essential for addressing global challenges related to food security, environmental conservation, and economic stability. Effective pest and weed management are critical to maintaining agricultural productivity and quality. Traditional methods, predominantly reliant on chemical pesticides and herbicides, often result in environmental degradation, resistance development, and economic burdens. This research paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in pest and weed management, emphasizing their potential to enhance precision, efficiency, and sustainability in agricultural practices. Through a comprehensive literature review, the development of a conceptual framework, and the application of robust methodologies, this study demonstrates how AI and ML can revolutionize pest and weed control strategies. The results indicate significant improvements in detection accuracy, predictive capabilities, resource optimization, and overall crop yield and quality. The findings suggest that AI and ML-driven approaches are pivotal in advancing sustainable agriculture, offering scalable and environmentally friendly solutions to mitigate the adverse effects of pests and weeds.