Artificial Intelligence–Driven Decision Support Systems for Enhancing Sustainable Crop Productivity

Abstract

Climate variability, pressure on available resources and increasing demands on food further erode efficacious agricultural systems that require intelligent and adaptive decision-making systems. Artificial intelligence (AI) has become an offering with potential to complement all types of information to integrate into actionable insights (data-driven agricultural management). In this research, empirical research and analytical modeling are applied to discuss the importance of AI-driven decision support systems (DSS) in enhancing the sustainable crop productivity. The study utilizes multi-season field observations, agro- meteorolog data, soil health metrics, remote sensing data, and management records of farmers in the variety of agro-climatic regions. The machine learning models were used to assist with the key farming decisions in terms of forecasting yields, when to irrigate, identifying pests and diseases, and optimization of nutrients. The traditional standard statistical measures of accuracy were to determine model performance, whereas the sustainability performance was measured in terms of the stability of yields, water-use efficiency, and input optimization measures. The findings indicate that AI-powered DSS are associated with a high level of accuracy in decision-making in comparison to traditional methods of advisory, which results in better performance of crops, less wastage of resources, and environmental stress reduction. The results also indicate that the situational calibration, the trustworthiness of the data and the participation of farmers are significant parameters determining whether the systems will work or not. This work can add to the existing literature on AI-based decision frameworks and on the agronomic and sustainability advantages of AI-supported frameworks by empirically showing these benefits. The study highlights the future of AI-based DSS as a strategic mechanism of promoting resilient, resource-efficient, and sustainable agricultural production systems.