PestGPT: Integrating Visual Intelligence and ChatGPT into a Mobile Solution for Sustainable Pest Management

PestGPT：将视觉智能和 ChatGPT 集成到可持续害虫管理的移动解决方案中

• 批准号: 10076558
• 金额: $ 6.35万
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10076558
• 关键词: PestGPT; Integrating; Visual; Intelligence; ChatGPT

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.Effective IPM deployment is dependent on accurate pest identification and quantification, accurate pest identification and quantification, placing this pest information into a crop tolerance threshold context, and deploying sustainable and economically sound pest control strategies. 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 mobile visual intelligence with ChatGPT technology into an improved pest management solution that:* Offers rapid detection and quantification of crop pests using mobile devices.* Places pest quantification into context of regionally relevant pest tolerance thresholds.* Provides estimation of economic thresholds and useful advice on crop pest management.To achieve this we will expand the pest detection model to above-ground pests of rapeseed and potato: 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 estimation of economic thresholds and useful advice on crop pest management. AI-model development will be led by The University of Sheffield; provision of pest management advice will be led by ADAS; 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。 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.Effective IPM deployment is dependent on accurate pest identification and quantification, accurate pest identification and quantification, placing this pest information into a crop tolerance threshold context, and deploying sustainable and economically sound pest control strategies.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，我们建议通过将移动视觉智能与Chatgpt技术扩展到改进的害虫管理解决方案中，以基于该项目的成功，该解决方案：*提供了使用移动设备对作物害虫进行快速检测和定量。和马铃薯：卷心菜蚜虫，桃红色蚜虫，马铃薯蚜虫和卷心菜茎跳蚤甲虫，以及这些害虫子集的测试和验证阈值。您的主要输出将是一个智能型app，它将提供PEST检测支持，突出显示识别的PEST的阈值，并为经济阈值提供了限制的阈值信息，并提供了对经济阈值的信息。 AI模型的开发将由谢菲尔德大学领导；提供有害生物管理建议的建议将由ADA领导； Smart-App用户界面的开发将由Mutus Tech Ltd领导。