Smart AgroClimate: Leveraging Machine Learning for Integrated Weather and Soil Intelligence to Drive Sustainable, Climate-Resilient Precision Agriculture

Accurate soil moisture estimation is essential for farm operations and water resource management. Direct soil moisture models are complex due to factors like soil type, crops, and weather conditions. This paper explores machine learning (ML) techniques to select relevant soil and weather features, based on agricultural and ML studies, and compares them with existing patents to design a cost-effective weather station. Field experiments demonstrate the potential to optimize resource use, maximize crop yields, and support sustainable agriculture. The research highlights ML’s role in soil nutrient management, fertilizer guidance, and crop quality assessment. It emphasizes intelligent irrigation scheduling through ML-based control strategies, benefiting economic, social, and environmental aspects. The paper presents a general architecture for smart agriculture, reviews datasets and techniques, addresses challenges with long-term data processing, and suggests future research for scalable, domain-independent irrigation management and resource optimization.