Integrated Pest Management (IPM) techniques have been widely used in agriculture to manage pest damage in the most economical way and to minimise harm to people, property and the environment. However, current research and products on the market cannot consolidate this process. Most existing solutions either require experts to visually identify pests or cannot automatically assess pest levels and make decisions based on detection results. To make the process from pest identification to pest management decision making more automated and intelligent, we propose an end-to-end integrated pest management solution that uses deep learning for semi-automated pest detection and an expert system for pest management decision making. Specifically, a low computational cost sampling point generation algorithm is proposed to enable mobile devices to generate uniformly distributed sampling points in irregularly shaped fields. We build a pest detection model based on YoloX and use Pytorch Mobile to deploy it on mobile phones, allowing users to detect pests offline. We develop a standardised sampling specification and a mobile application to guide users to take photos that allow pest population density to be calculated. A rule-based expert system is established to derive pest management thresholds from prior agricultural knowledge and make decisions based on pest detection results. We also propose a human-in-the-loop algorithm to continuously track and update the validity of the thresholds in the expert system. The mean average precision of the pest detection model is 58.17% for 97 classes, 75.29% for 2 classes, and 57.33% for 11 classes on three pest datasets, respectively. The usability of the pest management system is assessed by the User Experience Surveys and achieves a System Usability Scale (SUS) score of 76. The usability of the proposed solution is validated by qualitative field experiments.

中文翻译：

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以农民为中心的有效移动智能解决方案，使用轻量级深度学习进行小麦病虫害综合管理


综合害虫管理 （IPM） 技术已广泛用于农业，以最经济的方式管理害虫危害，并最大限度地减少对人员、财产和环境的危害。然而，目前市场上的研究和产品无法整合这一过程。大多数现有解决方案要么需要专家直观地识别害虫，要么无法自动评估害虫水平并根据检测结果做出决策。为了使从害虫识别到害虫管理决策的过程更加自动化和智能，我们提出了一种端到端的集成害虫管理解决方案，该解决方案使用深度学习进行半自动害虫检测，并使用专家系统进行害虫管理决策。具体来说，提出了一种低计算成本的采样点生成算法，使移动设备能够在不规则形状的场中生成均匀分布的采样点。我们基于 YoloX 构建了虫害检测模型，并使用 Pytorch Mobile 将其部署在手机上，让用户可以离线检测害虫。我们制定了标准化的采样规范和移动应用程序，以指导用户拍摄照片，从而计算害虫种群密度。建立了一个基于规则的专家系统，从先前的农业知识中得出害虫管理阈值，并根据害虫检测结果做出决策。我们还提出了一种人机回圈算法，以持续跟踪和更新专家系统中阈值的有效性。在 3 个害虫数据集上，害虫检测模型的平均精度分别为 97 个类的 58.17%、2 个类的 75.29% 和 11 个类的 57.33%。 害虫管理系统的可用性由用户体验调查评估，系统可用性量表 （SUS） 得分为 76。所提出的解决方案的可用性通过定性现场实验得到验证。