<![CDATA[Agricultural production increasingly faces climate variability, resource scarcity, environmental degradation, and rising demands for sustainable food systems, creating an urgent need for precise and adaptive crop management. This study examines how integrated drones, wireless field sensors, and artificial intelligence can enhance precision crop management by transforming heterogeneous agricultural data into actionable decision intelligence. A mixed-methods field-based design combined quasi-experimental comparisons, drone-based remote sensing, continuous sensor monitoring, multimodal data integration, artificial intelligence modeling, agronomic measurements, and stakeholder interviews to evaluate technological and operational performance. The findings indicate that integrated digital management improved crop-stress detection, accelerated management responses, increased productivity, and enhanced water, fertilizer, and pesticide-use efficiency compared with conventional practices. Multimodal integration generated stronger predictive performance than isolated technologies by combining spatial, temporal, and environmental information, while human validation remained essential for adapting algorithmic recommendations to field conditions. The study concludes that effective precision agriculture depends not on technology quantity but on integration quality and the capacity to translate data into timely, interpretable, and contextually relevant interventions. The proposed Integrated Agricultural Digital Intelligence Framework establishes a continuous Sense–Integrate–Analyze–Decide–Act–Learn cycle, providing a foundation for adaptive, resource-efficient, and sustainable crop management across diverse agricultural contexts worldwide.]]>
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