Remote Sensing of Strawberry Plants Using UAVs and Deep Learning

Abstract

Abstract Background To address the challenge of real-time plant monitoring in greenhouse environments, this industry-driven research focuses on developing an autonomous quadrotor UAV system specifically designed for monitoring strawberry plants. Traditional methods for greenhouse monitoring are labor-intensive and lack scalability, particularly in precision agriculture applications. Method The study begins by proposing the mature strawberry detection model for greenhouse environment. The YOLOv9 with GLEAN advantage is proposed to detect small mature strawberries via on board camera on the quadrotor. Also the hybrid trajectory tracking controller for quadrotor is proposed and validated in both simulation and real time environment. The UAV follows predefined way points for navigation in the greenhouse environment. An onboard vision system is integrated, employing a novel YOLOv9-GLEAN-based algorithm for online and offline mature strawberry detection and counting. Results The YOLOv9-GLEAN model achieves high detection accuracy, as confirmed by evaluation metrics such as precision, recall, and F1-score. The proposed hybrid (PID+LQR) controller demonstrates superior tracking performance compared to other conventional controllers. The integrated control and perception system proves effective in both simulated and real-world greenhouse environments. Discussion The research validates the efficacy of deep learning models, with YOLOv9-GLEAN showing exceptional performance in enabling rapid, precise, and automated detection of ripe strawberries through quadrotor deployment in greenhouse environments. Such agricultural monitoring technologies represent a substantial advancement beyond conventional manual inspection approaches, empowering farmers and greenhouse operators to execute well-informed, time-sensitive management decisions that minimize crop losses and optimize production yields. This investigation underscores the revolutionary impact that deep learning technologies can have within greenhouse agriculture.

Plant phenotyping relevance

温室イチゴの成熟果実を画像認識で検出・計数する手法とUAV搭載システムが研究の中心であり、果実の成熟状態・数量という植物器官の形質を抽出しているため含める。

Code and data availability

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