Artificial Intelligence in Agriculture: Bridging Accuracy, Labour Challenges and Farmer Knowledge Gaps

Abstract

Artificial Intelligence (AI) is emerging as a transformative technology in modern agriculture, offering innovative solutions to improve productivity, efficiency, and sustainability. Increasing food demand, climate variability, depletion of natural resources, and labor shortages has exposed the limitations of traditional farming practices. In this context, AI-based technologies play a crucial role in enabling precision agriculture. Techniques such as machine learning, computer vision, predictive analytics, and Internet of Things (IoT)-enabled systems support real-time crop monitoring, precision irrigation, pest and disease detection, yield prediction, and soil analysis. Despite its significant potential, the large-scale adoption of AI in agriculture faces several challenges. Limited availability of high-quality datasets, inadequate digital infrastructure in rural areas, high implementation costs, and a lack of technical knowledge among farmers restrict widespread implementation. Additionally, concerns related to data ownership, ethical use of technology, and the impact on agricultural labor must be carefully addressed. This study analyzes current AI applications in agriculture and evaluates their effectiveness in real-world conditions. It also highlights the need for policy support, farmer training, and digital capacity building to ensure sustainable and inclusive integration of AI in the agricultural sector