UK-China Agritech Challenge - Utilizing Earth Observation and UAV Technologies to Deliver Pest and Disease Products and Services to End Users in China

中英农业科技挑战赛——利用地球观测和无人机技术为中国最终用户提供病虫害产品和服务

• 批准号: BB/S020977/1
• 负责人: Martin Wooster
• 金额: $ 42.14万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FS020977%2F1
• 关键词: UK; China; Agritech; Challenge; Utilizing

(KCL) This project aims to develop time-series tools for pest and disease monitoring, forecasting and management in China, providing service products at national and local levels to enhance pest and disease control of wheat rust and locusts in particular. It will also develop advanced UAV-based tools that provide more efficient spraying of control measures (biopesticides) to provide alleviation of these problems without causing chemical pollution. In this context, the King's team will be developing ways to downscale satellite-based maps of land surface temperature to scales more akin to those of the fields within which the crops grow, in order to aid the development of mathematical models that can be used to forecast and monitor the efficacy of the bio-control measures and the spread of wheat rust. They will develop UAV-based methods to deliver maps of crop parameters from aerial imagery, which will be used to help both development of the downscaled satellite datasets and to provide an understanding of the crop structures that can be used to help in the development of the spraying technologies and planning tools that will be developed for the aerial platforms. Finally they will also develop methods to remotely sense locust surface temperatures from thermal imaging, in order to contribute to the development of better models of locust internal body temperature upon which the final mathematical models of bio-pesticide development rate depends.(Loughborough) Our proposal aims to develop a long term sustainable innovative partnership in agriculture technology between the UK and China through a comprehensive approach to deal with these two major agricultural pests/diseases. It will do so from a monitoring,forecasting and management perspective, combining cutting edge technology, modelling and biological information.The project is structured under six work packages that follow the cycle of a dynamic Agri-Tech service: observe to understand the nature of the problem and locate the pest/crop problem (WP1), orientate through development of forecast models to provide strategic risk awareness (WP2), decide providing useful information where to control pests at national and local levels (WP3), and act locally using precise application of bio pesticides via UAV deployments (WP4). The scopeof the project primarily falls into Agri-Tech Challenge 1 "Precision agriculture, agriculture digitisation and decision management tools" but also makes significant contributions to Challenge 2 "Improving the efficiency of sustainable agriculture". A key theme is to develop technologies for integrating data collected by UAVs, earth observation satellites,and bioscience applications related to disease/pest modelling. The project will develop autonomous and smart planning tools for agricultural remote sensing and plant protection, ultimately for the benefit of end users to reduce the cost and improve the effectiveness of their operations. One of the key outcomes is the development and application of novel technical systems for the monitoring and prediction of crop disease/pest outbreaks, As a novel technology, biopesticides treatment of orthoptera will be investigated and demonstrated, along with the modelling and prediction of yellow rust and orthoptera, real-time remote sensing methods will facilitate time specific and site specific treatment along with improved general farming management. Combining this with the work on biopesticides will significantly reduce the use of chemical pesticides and the risk of the development of crop's resistance to them, and will increase biodiversity due to lack of chemical pesticides. In addition to these benefits, the project will open up new business opportunities for both the UK, and Chinese industrial partners outside of China.

(KCL)该项目旨在开发中国病虫害监测、预测和管理的时间序列工具，在国家和地方两级提供服务产品，以加强特别是小麦锈病和蝗虫的病虫害防治。它还将开发先进的无人机工具，提供更有效的控制措施（生物农药）喷洒，以减轻这些问题，而不会造成化学污染。在这方面，King的团队将研究如何将基于卫星的地表温度地图缩小到更接近作物生长的田地的尺度，以帮助开发可用于预测和监测生物防治措施的效力和小麦锈病传播的数学模型。他们将开发基于无人机的方法，从航空图像中提供作物参数地图，这将用于帮助缩小卫星数据集的开发，并提供对作物结构的了解，可用于帮助开发将为航空平台开发的喷洒技术和规划工具。最后，他们还将开发通过热成像遥感蝗虫表面温度的方法，以帮助开发更好的蝗虫体内温度模型，生物农药开发速度的最终数学模型将依赖于此。（拉夫堡）我们的建议旨在通过全面的方法来应对这两种主要的农业病虫害，在英国和中国之间建立长期可持续的农业技术创新伙伴关系。它将从监测、预测和管理的角度出发，结合尖端技术、建模和生物信息。该项目分为六个工作包，遵循动态农业技术服务的周期：观察以了解问题的性质，并确定虫害/作物问题（WP 1），通过开发预测模型进行定位，以提供战略风险意识（WP 2），决定在国家和地方各级提供防治害虫的有用信息（WP 3），并通过无人机部署在当地采取行动，精确施用生物农药（WP 4）。该项目的范围主要福尔斯属于农业技术挑战1“精准农业、农业数字化和决策管理工具”，但也为挑战2“提高可持续农业的效率”做出了重大贡献。一个关键主题是开发整合无人机、地球观测卫星收集的数据和与疾病/害虫建模有关的生物科学应用的技术。该项目将开发用于农业遥感和植物保护的自主和智能规划工具，最终使最终用户受益，以降低成本并提高其运营效率。主要成果之一是开发和应用新的技术系统，用于监测和预测作物病害/害虫爆发。作为一项新技术，将研究和示范直翅目生物农药处理，沿着黄锈病和直翅目的建模和预测，实时遥感方法将促进特定时间和特定地点的处理，沿着改进的一般农业管理。将其与生物农药的工作相结合，将大大减少化学农药的使用和作物对它们产生抗药性的风险，并将增加由于缺乏化学农药而产生的生物多样性。除了这些好处，该项目还将为英国和中国以外的中国工业合作伙伴开辟新的商机。

项目成果

Experimental evaluation of UAV spraying for peach trees of different shapes: Effects of operational parameters on droplet distribution

• DOI: 10.1016/j.compag.2020.105282
• 发表时间: 2020-03-01
• 期刊: COMPUTERS AND ELECTRONICS IN AGRICULTURE
• 影响因子: 8.3
• 作者: Meng, Yanhua;Su, Jinya;Lan, Yubin
• 通讯作者: Lan, Yubin

Spraying Coverage Path Planning for Agriculture Unmanned Aerial Vehicles

农业无人机喷洒覆盖路径规划

• DOI: 10.23919/icac50006.2021.9594271
• 发表时间: 2021
• 作者: Guo Y

The influence of rotor downwash on spray distribution under a quadrotor unmanned aerial system

• DOI: 10.1016/j.compag.2022.106807
• 发表时间: 2022-05
• 期刊: Comput. Electron. Agric.
• 作者: M. Coombes;Sam Newton;James Knowles;A. Garmory