Knowledge Distillation empowered Mobile Intelligence Solution for Sustainable Management of Crop Pests and Soil Health

在世界范围内，农作物受到无脊椎动物害虫的威胁，这些害虫会造成取食损害并传播植物病毒。高水平的虫害可造成高达80%的产量损失。虽然杀虫剂经常用于农作物保护，但不加控制的使用可能会造成土壤侵蚀和污染。虫害的可持续管理有赖于：准确识别虫害的存在，了解可容忍的虫害程度，以及有效的虫害管理解决方案，以维持土壤健康。目前，英国还没有可持续管理小麦病虫害的综合田间解决方案。随着对杀虫剂使用限制的增加和更多抗药性害虫种群的出现，种植者正在寻求更可持续的综合病虫害管理(IPM)实践。然而，有许多障碍限制了对综合病虫害管理原则的采用：准确识别无脊椎动物有害生物是困难的，需要分类学培训，这是种植者经常缺乏的一项技能；目前的阈值在田间条件下几乎没有得到测试和验证，限制了种植者的信心等。在一个初始项目中，我们开发了这个问题的早期解决方案，方法是建立一个人工智能驱动的移动有害生物检测解决方案来识别小麦作物中的有害生物(创新项目10002902)。*将小麦病虫害检测模型扩展到马铃薯和油菜籽等高级作物。*通过优化机制提高在手机上运行的模型的识别精度和效率。*评估和验证重点作物病虫害的可接受阈值。在此，我们将提出一个后续项目，通过优化具有知识蒸馏技术的深度学习检测模型来优化我们的人工智能驱动的移动病虫害管理解决方案。其成果将是增强的MPM解决方案，提供：1)使用移动设备快速检测和准确量化叶面害虫；2)将害虫量化纳入特定地区的害虫耐受阈值；3)提供对经济阈值的估计和害虫控制建议。它将建立在该联盟现有资源的基础上，包括：10K小麦病虫害图像(Mutus-Tech)、世界领先的病虫害检测模型(PestNet；UoS)、农艺和病虫害管理专业知识(ADAS)。这一新解决方案将提高农场的应变能力，减少不必要的杀虫剂使用，并改善病虫害防治。这项技术还将降低农民日益增长的成本。最终，它将提高农业生产率和利润，并刺激英国市场的增长。

Worldwide, crops are threatened by invertebrate pests which cause feeding damage and transmit plant viruses. High levels of infestation can cause up to 80% yield loss. While pesticides are often applied to crops protection, their uncontrolled application can cause soil erosion and contamination. Sustainable management of pest is reliant on: accurate identification of the pest present, knowledge of the levels of pest damage that can be tolerated, and effective pest management solutions for maintaining soil health. Currently, there is no integrated in-field solution for sustainable management of wheat pest in the UK.As restrictions on insecticide use increase and a greater number of insecticide resistant pest populations emerge, growers are looking towards more sustainable integrated pest management (IPM) practices. However, there are numerous barriers that restrict the uptake of IPM principles: Accurate identification of invertebrate pests is difficult and requires taxonomic training, a skill that growers often lack; current thresholds have received little testing and validation under field conditions, limiting grower confidence, etc. In an initial project we developed an early-stage solution to this problem by building an AI-driven mobile pest-detection solution to identify insect pests in wheat crops (Innovate project 10002902). The outcome could be further improved to be more successful with benefits below:* Expanding wheat pest detection model to above-group crops like potato and rapeseed.* Improving recognition accuracy and efficiency of models running in mobile phones with optimisation mechanisms.* Evaluating and validating the accepted pest thresholds for the focal crop pests.Here, we will propose an follow-on project that improves our AI-driven mobile pest management solution through optimising deep learning detection models with knowledge distillation technique to pests of other arable crops. The output will be an enhanced MPM solution that offers: 1) rapid detection and accurate quantification of foliar pests using mobile devices; 2) placing pest quantification into context of region-specific pest tolerance thresholds; 3) Providing estimation of economic thresholds and advice on pest control. It will build on existing resources in the consortium, including: 10K wheat pest images (Mutus-Tech), a world-leading pest detection model (PestNet; UoS), agronomic and pest management expertise (ADAS).This new solution will improve farm resilience, reduce unnecessary insecticide use, and improve pest control. The technology will also reduce growing costs for farmers. Ultimately, it will improve farm productivity and profits, and stimulate growth of the UK market.