Chapter Four - Operational and analytical frameworks of Unoccupied Aerial Vehicles (UAV) for precision agriculture applications

Abstract

Agricultural systems are entering an era defined not just by mechanization but by real-time, spatially aware, data driven production practices. This shift which has rapidly evolved from novelty to necessity in precision agriculture. At the center of this shift Unoccupied Aerial Vehicles (UAV) or drones have operationalized high-resolution aerial data into sustainable agricultural outcomes. This chapter provides an application-focused roadmap for integrating UAV into modern agronomic workflows as pre, during, and post flight operations for agronomic flight planning. It bridges the engineering of flight platforms and sensors with the applications in crop stress detection, variable-rate input application, and predictive yield modeling. We explore UAV architecture and their implications for data resolution, field scale, and operational complexity. Sensor systems use the portions of electromagnetic spectrum (RGB, multispectral, hyperspectral, thermal, LiDAR) and are examined through their applications for plant physiology and soil interactions. Ground sampling distance, spectral calibration, geospatial accuracy and photogrammetry are not treated as ancillary steps, but as critical determinants of agronomic utility. We describe the full data pipeline from FAA (Federal Aviation Administration) regulations to machine learning-driven analytics, acquisition, orthomosaic generation, digital surface modeling, vegetation index extraction, and the development of actionable prescription maps. In the context of AI evolution, we emphasize how AI-ML methods classification, regression, clustering, and dimensionality reduction help with integrating complex patterns in time-series UAV imagery, enabling early and precise management of nutrients, water, weeds, and disease. By synthesizing global regulatory frameworks and field-based use cases, the chapter concludes UAV as tools that transforms data into agronomic decisions.

Plant phenotyping relevance

UAVセンサー、校正、フォトグラメトリ、オルソモザイク、植生指数抽出、機械学習解析を含む一連の植物状態・ストレス推定ワークフローをレビューしており、単なる生物学的実験の測定ではなく、取得・解析手法とプラットフォームが中心です。

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