Adaptive AI-enabled and Context-enhanced Mobile Intelligence for Climate-smart Pest Management to Optimise Sustainable and Resilient Farming

自适应人工智能支持和环境增强的移动智能，用于气候智能型害虫管理，以优化可持续和有弹性的农业

• 批准号: 10050919
• 金额: $ 48.66万
• 依托单位: MUTUS TECH LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10050919
• 关键词: Adaptive; AI; enabled; Context; enhanced

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. Currently, farmers are advised to follow economic thresholds and to apply management interventions when thresholds are exceeded. Generally, thresholds are defined as the level of pest infestation above which it is expected the crop will suffer economic damage. 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.To effectively deploy IPM practices three components are required: 1) accurate identification of the pest(s) present; 2) accurate information on thresholds and their efficacy; 3) information on insecticide resistance of the local/regional pest population. 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; and insecticide resistance information for key pests is spatially-limited and primarily provided on a national basis.The central barrier for IPM uptake is lack of grower confidence in their ability to identify a pest. In a recent project we developed an early-stage solution to this problem by building an AI-driven pest-detection model to identify insect pests in wheat crops (Innovate project 10002902). Here, we propose to build on the success of this project by expanding the AI-driven pest-detection model to pests of other arable crops and by integrating more information into the end-user output in order to address the other barriers to IPM uptake. To achieve this we will expand the pest detection model to above-ground pests of rapeseed and potato, integrate region-specific insecticide resistance status for key pests: cabbage aphid, peach-potato aphid, potato aphid, and the cabbage stem flea beetle, and test and validate thresholds for a subset of these pests.Our main output will be a smart-app that provides pest detection support, highlights the current threshold for the identified pest, and provides information on the insecticide resistant status of regional pest populations. AI-model development will be led by The University of Sheffield; provision of pest management advice and threshold testing will be led by ADAS; insecticide resistance testing will be led by The University of Liverpool; and the development of the smart-app user-interface will be led by Mutus Tech Ltd.

在全球范围内，农作物受到无脊椎动物害虫的威胁，这些虫害会造成喂养损害并传播植物病毒。高水平的侵染可能会导致多达80％的收益率损失。目前，建议农民遵循经济阈值，并在超过阈值时采用管理干预措施。通常，阈值定义为预期农作物将遭受经济损害的害虫侵染水平。随着对杀虫剂使用的限制增加，并且出现了更多抗杀虫剂的害虫种群，种植者正在寻求更可持续的综合有害生物管理（IPM）实践。为了有效地部署IPM实践，需要三个组件：1）准确识别存在的虫害； 2）有关阈值及其功效的准确信息； 3）有关局部/区域害虫种群杀虫剂耐药性的信息。但是，有许多障碍限制了IPM原则的吸收：准确识别无脊椎动物害虫是困难的，需要分类学训练，这是种植者经常缺乏的技能；当前的阈值在现场条件下几乎没有进行测试和验证，从而限制了种植者的信心。关键害虫的杀虫剂耐药性信息是空间限制的，主要是在国家基础上提供的。I​​PM摄取的中心障碍是对鉴定害虫的能力缺乏种植者的信心。在最近的一个项目中，我们通过建立AI驱动的害虫检测模型来识别小麦作物中的害虫（Innovate Project 10002902），开发了一种早期解决方案。在这里，我们建议通过将AI驱动的害虫检测模型扩展到其他可耕作作物的害虫，并将更多信息集成到最终用户输出中，以解决其他障碍IPM摄取的障碍，从而建立在该项目的成功基础上。为了实现这一目标，我们将将害虫检测模型扩展到上面的腌菜和马铃薯的斑点，整合特定区域的杀虫剂的耐杀虫剂状态：卷心菜蚜虫，桃花蚜虫，桃红色蚜虫，马铃薯蚜虫，蚜虫和卷心菜的干甲虫，以及测试和验证这些主要的pests Appss smarts Appss。已确定的害虫的阈值，并提供有关区域害虫种群抗杀虫剂状态的信息。 AI模型的开发将由谢菲尔德大学领导；提供有害生物管理的建议和阈值测试将由ADA领导；杀虫剂耐药性测试将由利物浦大学领导； Smart-App用户界面的开发将由Mutus Tech Ltd领导。