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This study investigates the transformative potential of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) sensor networks in revolutionizing climate-resilient agriculture. It explores their combined impact on yield forecasting accuracy, precision farming interventions, and socio-technical scalability, offering insights into how this synergy addresses the inadequacies of conventional agricultural paradigms. Using an integrative framework, this research examines IoT-LiDAR applications across diverse agroecological contexts, emphasizing machine learningMachine learning innovations, interoperability protocols, and farmer-centric governance models. It analyzes IoT-LiDAR’s ability to enhance predictive analytics through real-time environmental monitoring, structural crop diagnostics, and adaptive decision-making. The integration achieves a 28–32% improvement in yield predictionYield prediction accuracy, enabling rapid detection of waterlogging and canopy stress to reduce crop losses by 15–25%. Machine learning frameworks like the Decision Support System for Agrotechnology Transfer (DSSAT) combined with reinforcement learning resolve algorithmic opacity, increasing farmer trust by 22%. The Agricultural Data Interchange Protocol ADIP standardizes data fusion across sensor networks, cutting integration costs by 35%. Policy-aligned subsidies for photogrammetric drones and modular sensor kits democratize access for smallholders, while decentralized edge computing reduces cloud dependency by 60%. This study redefines agricultural innovation by linking IoT-LiDAR’s technical precision with socioeconomic inclusivity, advocating for crop-agnostic AI models, adaptive governance, and climate-resilient infrastructure to bridge global food securityFood security gaps. This research pioneers a framework to harmonize IoT-LiDAR analytics with participatory design, providing a blueprint for equitable climate adaptation that balances technological advancement with the socioeconomic realities of smallholder farming communities.

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LiDAR and IoT Sensor Networks for Climate-Aware Yield Forecasting in Agriculture

  • 章节

  • 发表日期：

    2026年

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  • Madhusudan Narayan,
  • Nishant Mani,
  • Tanmoy Chakraborty

摘要

This study investigates the transformative potential of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) sensor networks in revolutionizing climate-resilient agriculture. It explores their combined impact on yield forecasting accuracy, precision farming interventions, and socio-technical scalability, offering insights into how this synergy addresses the inadequacies of conventional agricultural paradigms. Using an integrative framework, this research examines IoT-LiDAR applications across diverse agroecological contexts, emphasizing machine learningMachine learning innovations, interoperability protocols, and farmer-centric governance models. It analyzes IoT-LiDAR’s ability to enhance predictive analytics through real-time environmental monitoring, structural crop diagnostics, and adaptive decision-making. The integration achieves a 28–32% improvement in yield predictionYield prediction accuracy, enabling rapid detection of waterlogging and canopy stress to reduce crop losses by 15–25%. Machine learning frameworks like the Decision Support System for Agrotechnology Transfer (DSSAT) combined with reinforcement learning resolve algorithmic opacity, increasing farmer trust by 22%. The Agricultural Data Interchange Protocol ADIP standardizes data fusion across sensor networks, cutting integration costs by 35%. Policy-aligned subsidies for photogrammetric drones and modular sensor kits democratize access for smallholders, while decentralized edge computing reduces cloud dependency by 60%. This study redefines agricultural innovation by linking IoT-LiDAR’s technical precision with socioeconomic inclusivity, advocating for crop-agnostic AI models, adaptive governance, and climate-resilient infrastructure to bridge global food securityFood security gaps. This research pioneers a framework to harmonize IoT-LiDAR analytics with participatory design, providing a blueprint for equitable climate adaptation that balances technological advancement with the socioeconomic realities of smallholder farming communities.