发表机构
Monash University; The University of Melbourne; Western Sydney University; University of New England（莫纳什大学; 墨尔本大学; 西悉尼大学; 新英格兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AGRICAM履带式授粉监测机器人，其可自主运行并在商业蓝莓农场成功监测传粉活动，为授粉管理提供数据支撑，助力提升作物产量与粮食安全。
AI 中文摘要
昆虫授粉对全球粮食生产至关重要，但在商业农场规模下监测传粉者仍是一项挑战。计算机视觉与深度学习的最新进展已能对传粉者行为进行详细分析，但监测工作需在细节、空间覆盖范围及人力或技术资源间进行权衡。本文提出了专为满足保护栽培系统中大规模授粉监测需求设计的专用机器人系统——AGRICAM（昆虫与作物活动监测自动导引机器人）。AGRICAM 可在低成本、易安装的轨道上自主运行，沿作物行移动，不会干扰农场作业或昆虫行为。该平台集成了两台 RGB 相机、微气候传感器、GPS、RFID 模块、运动传感器及用于数据传输的 4G 蜂窝网络连接，网页界面支持远程设备配置与调度。系统自主捕获昆虫位置及当地环境条件的视频与图像数据，这些数据被传输至云端并通过计算机视觉模型分析，以量化传粉者访花情况及时空活动变化。我们在商业蓝莓农场部署该系统以验证其能力，它成功在 30 小时内绘制了 80 米长工业塑料大棚内的昆虫授粉模式，相关数据支持的空间分析确认了大棚内传粉者分布均匀（符合农场管理团队需求），还凸显了昆虫活动随一天中时间及微气候的变化。研究表明，AGRICAM 是一种可扩展、自动化的作物授粉监测设备，能支持数据驱动决策以优化授粉管理，进而提升作物生产力与粮食安全。
英文摘要
Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or technological resources. This paper presents the Automated Guided Robot for Insect and Crop Activity Monitoring (AGRICAM), a purpose-built robotic system designed to meet the requirements of large-scale pollination monitoring in protected cropping systems. AGRICAM operates autonomously on low-cost, easily installed track for movement along crop rows, without disrupting farm operations or insect behaviour. The platform integrates two RGB cameras, microclimate sensors, GPS and RFID modules, motion sensors, and 4G cellular network connectivity for data transmission. A web interface enables remote device configuration and scheduling. The system autonomously captures video and image data of insects' locations and local environmental conditions. These are transferred to the cloud and analysed using computer vision models to quantify pollinator visitation and spatio-temporal activity variation. We deployed the system on a commercial blueberry farm to demonstrate and test its capability. It successfully mapped insect pollination patterns across 80 m long industrial polytunnels over 30 hours. This data enabled spatial analyses of insect activity we used to confirm a uniform pollinator distribution within polytunnels, as desired by the farm management team. The data also highlighted variation of insect activity associated with time of day and microclimate. AGRICAM therefore has been shown to be a scalable, automated crop pollination monitor that can support data-driven decisions to enhance pollination management, thereby improving crop productivity and food security.
Comments20 pages, 8 figures
