High-Frequency, real-time parcel-level agricultural monitoring Framework: Integrating Tower-Based cameras and artificial intelligence

Abstract

Traditional remote sensing approaches for agricultural monitoring are frequently constrained by limited temporal resolution, discontinuous coverage, and high operational costs. These limitations hinder the delivery of high-frequency, continuous, and unmanned agro-information that is critical for precision agriculture—especially in smallholder and autonomous farming scenarios. Meanwhile, communication towers equipped with video cameras are ubiquitously deployed in rural landscapes yet remain underutilized for agricultural applications. This study presents a high-frequency, tower-based, unmanned agricultural monitoring framework that progressively addresses three fundamental challenges, thereby enabling real-time, parcel-level agro-monitoring. In particular, our framework tackles: (1) accurate geo-referencing of oblique imagery through a quaternion-based geographic coordinate transformation, which precisely maps video streams to field parcels; (2) robust segmentation of cultivated parcels via a GIS-guided approach that integrates field boundary data with the Segment Anything Model (SAM) for automatic delineation; and (3) comprehensive temporal intelligence by leveraging a large language model (LLM)-based recognition strategy that fuses time-series imagery, crop rotation history, and field management data to identify crop types, growth stages, farming operations, and anomalies at the parcel level. The resulting hierarchical framework combines front-end local processing for initial event detection with cloud-based advanced analysis, enhancing operational efficiency while ensuring rapid responses. Field deployments in three agricultural regions of China demonstrate that the framework supports automated, hourly-to-daily monitoring of farming activities and crop conditions within a 1–2 km radius of each tower, with key agricultural events, growth stage transitions, and parcel-level anomalies tracked and mapped in near real time. Compared to conventional satellite and aerial remote sensing, this approach offers substantial improvements in monitoring frequency, spatial continuity, and labor efficiency.

Plant phenotyping relevance

塔載カメラ画像とAIによって作物の生育段階・異常などの植物状態を時系列推定する監視基盤が研究の中心であり、単なる農業実験のルーチン測定ではない。ただし作業監視や作物識別も含むため、植物表現型への焦点は部分的である。

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