Semi-Automated Monitoring of Western Flower Thrips

西花蓟马的半自动监测

• 批准号: 86740
• 金额: $ 12.73万
• 依托单位: RAZBIO LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=86740
• 关键词: Semi; Automated; Monitoring; Western; Flower

Western Flower thrips (WFT) are an important agricultural pest of several crops including protected vegetables and soft fruit crops (1). WFT cause direct damage by feeding on the plant as well as through transmission of different diseases. WFT affected crops not only reduce the crop yield but also affect the crop quality e.g. deformed strawberries thereby reducing the market value of the crops. WFT have developed resistance to the most chemical insecticides and currently WFT control strategies are dependent on effective WFT monitoring and the use of its natural enemies e.g. predatory mites.Success of these natural enemies relies on their application at a time to coincide with the presence of susceptible life stages of WFT. Application of natural enemies in advance of the emergence of WFT leads to death of these enemies whilst a late application might not be enough to suppress the WFT population. Currently, farmers use manual monitoring of WFT to determine the pest population which is not only laborious but also result in false alarms or inaccurate data collection. Application of machine learning and artificial intelligence offers an opportunity to develop a semi-automated WFT monitoring systemThis project brings together artificial intelligence -based monitoring model to monitor WFT population. The monitoring tools will use pheromones to attract WFT to traps fitted with cameras and sensors. Different trap design and imaging techniques will be used to optimise the WFT capture and image quality for automated WFT monitoring. Data from these traps will help in identification and quantification of WFT hotspots enabling farmers to apply control measures timely and precisely.The project is timely as Covid-19 related restrictions and Brexit have limited the availability of skilled labour for manual WFT monitoring. Our innovation will fill this gap and help UK growers to produce quality strawberries.The project will help contribute significantly to reduce crop losses thus improving food production per unit area. The life cycle analysis will be used to demonstrate the effect of proposed technology in reducing carbon footprint of strawberry production. This study offers added value as technologies optimized here would offer potential to develop similar crop protection against a range of pest and diseases.1.van Lenteren J.C. & Loomans A.J.M. (1998) pests & diseases, vol 2, International conference, UK, pp 401--408

西花蓟马（WFT）是几种作物的重要农业害虫，包括保护蔬菜和软果作物(1)。WFT通过取食植物和传播不同的疾病造成直接损害。WFT不仅影响作物产量，而且影响作物品质，如草莓变形，从而降低作物的市场价值。白蛉已对大多数化学杀虫剂产生抗药性，目前的防治策略依赖于对白蛉的有效监测和利用其天敌，如掠食性螨。这些天敌的成功取决于它们的应用时间与WFT易感生命阶段的存在相吻合。在WFT出现之前施用天敌会导致这些敌人死亡，而晚施用可能不足以抑制WFT的数量。目前，农民使用人工监测WFT来确定害虫种群，这不仅费力，而且会导致假警报或不准确的数据收集。机器学习和人工智能的应用为开发半自动化的WFT监测系统提供了机会，该项目将基于人工智能的监测模型结合在一起，对WFT种群进行监测。监测工具将利用信息素吸引野生动物进入装有摄像头和传感器的陷阱。不同的陷阱设计和成像技术将用于优化WFT捕获和图像质量，以实现自动WFT监测。来自这些捕集器的数据将有助于确定和量化野虱的热点，使农民能够及时和准确地采取控制措施。该项目非常及时，因为与Covid-19相关的限制和英国脱欧限制了人工WFT监测的熟练劳动力的可用性。我们的创新将填补这一空白，帮助英国种植者生产高质量的草莓。该项目将大大有助于减少作物损失，从而提高单位面积粮食产量。生命周期分析将用于证明所提出的技术在减少草莓生产碳足迹方面的效果。这项研究提供了附加价值，因为这里优化的技术将为开发针对一系列病虫害的类似作物保护提供潜力。van Lenteren J.C. & Loomans A.J.M.（1998）病虫害，vol . 2, International conference， UK, pp 401—408