2024, Vol. 7, Issue 6, Part J

author(s)
Srikanthnaik J

abstract
The study systematically evaluates scholarly literature and technical reports to map the current state of AI-driven solutions in agriculture, including soil moisture sensing, autonomous weeding, drone-assisted spraying, and decision-support systems for efficient resource allocation. These intelligent systems enable farmers to minimize input costs, reduce environmental footprints, and enhance productivity by applying the right amount of water and chemicals at the right time and place. Moreover, the review discusses the potential of AI in overcoming challenges such as labor shortages, climate variability, and the demand-supply gap in global food production. It also identifies prevailing limitations in data quality, infrastructure, and farmer adoption, suggesting the need for policy support, training initiatives, and collaborative research. The insights derived from this review serve as a foundation for guiding future innovations and policy development aimed at achieving sustainable and smart agriculture.