用阈值法自动检测豌豆象，助力可持续农业
Threshold-Based Automated Pest Detection System for Sustainable Agriculture
- 基于灰度阈值与轮廓检测，依虫体大小实现识别
- 系统在资源受限环境下仍具显著检测效果
- 开源软件推动全球农业研究协作
本文介绍了一种基于阈值的自动化豌豆象检测系统，作为微软FarmVibes项目的一部分。该系统结合物联网（IoT）与计算机视觉技术，旨在监测和管理农田中的豌豆象种群，以提升作物产量并促进可持续农业实践。与基于机器学习的方法不同，本方法采用二值化灰度阈值与轮廓检测技术，依据豌豆象的尺寸特征进行识别。文中详细阐述了产品设计、系统架构、软硬件集成及整体技术策略。测试结果表明，该系统在豌豆象管理方面表现优异，并具备在资源受限环境中大规模部署的潜力。此外，相关软件已开源，供全球研究社区使用。
原文摘要 · Abstract (English)
This paper presents a threshold-based automated pea weevil detection system, developed as part of the Microsoft FarmVibes project. Based on Internet-of-Things (IoT) and computer vision, the system is designed to monitor and manage pea weevil populations in agricultural settings, with the goal of enhancing crop production and promoting sustainable farming practices. Unlike the machine learning-based approaches, our detection approach relies on binary grayscale thresholding and contour detection techniques determined by the pea weevil sizes. We detail the design of the product, the system architecture, the integration of hardware and software components, and the overall technology strategy. Our test results demonstrate significant effectiveness in weevil management and offer promising scalability for deployment in resource-constrained environments. In addition, the software has been open-sourced for the global research community.
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