Leveraging Artificial Intelligence and Iot for Precision Crop Management: Enhancing Physiological Responses in Stress-Prone Regions

Abstract

Farmers face many difficulties when managing crops in areas that are prone to stress, especially those that are impacted by temperature swings, pest invasions, and droughts. Traditional practices often fall short in mitigating these stresses, leading to reduced yields and increased susceptibility to diseases. This review examines how Internet of Things (IoT) technologies like soil moisture sensors, weather stations, and automated pest traps can be integrated with Artificial Intelligence (AI) models, such as artificial neural networks for scheduling irrigation and decision tree classifiers for predicting pests. These tools enable precise, real-time monitoring and adaptive management of crop systems. Case studies conducted in Nigeria focused on key arable crops like maize and tomatoes, where the application of AI-IoT systems resulted in a 15–20% increase in yields, enhanced drought resistance, and more efficient use of water and nutrients. Socio-economic benefits included reduced input costs and improved environmental sustainability. The review recommends future research on region-specific AI algorithms, field validation of integrated systems, and targeted policy interventions to support adoption among smallholder farmers. These steps are essential for scaling precision agriculture in vulnerable regions.