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

基本信息

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

项目摘要

(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、预报和管理的时间序列工具，为国家和地方各级提供服务产品，以加强对小麦锈病和蝗虫的病虫害防治。它还将开发先进的基于无人机的工具，提供更有效的喷洒控制措施（生物农药），从而在不造成化学污染的情况下缓解这些问题。在这种背景下，国王的团队将开发方法，将基于卫星的地表温度地图缩小到更接近农作物生长田地的比例，以帮助开发可用于预测和监测生物防治措施的效果和小麦锈病的传播。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，这将用于帮助开发缩小比例的卫星数据集，并提供对作物结构的了解，从而帮助开发将为空中平台开发的喷涂技术和规划工具。最后，他们还将开发通过热成像遥感蝗虫表面温度的方法，以有助于开发更好的蝗虫内部体温模型，生物农药开发速度的最终数学模型取决于该模型。（拉夫堡）我们的建议旨在通过综合方法应对这两种主要农业病虫害，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。该项目由六个工作包组成，遵循动态农业技术服务的周期：观察以了解农业技术的本质问题并定位害虫/作物问题（WP1），通过开发预测模型来提供战略风险意识（WP2），决定提供有用的信息，在国家和地方层面控制害虫（WP3），并在当地采取精确应用行动通过无人机生产生物农药部署（WP4）。该项目的范围主要属于农业技术挑战1“精准农业、农业数字化和决策管理工具”，同时也为挑战2“提高可持续农业效率”做出了重大贡献。一个关键主题是开发整合无人机、地球观测卫星和与疾病/害虫建模相关的生物科学应用收集的数据的技术。该项目将为农业遥感和植物保护开发自主智能规划工具，最终使最终用户受益，降低成本并提高运营效率。关键成果之一是开发和应用用于监测和预测作物病虫害爆发的新技术系统，作为一项新技术，将研究和示范直翅目生物农药处理以及黄锈病的建模和预测对于直翅目和直翅目，实时遥感方法将促进特定时间和特定地点的处理以及改善一般农业管理。将此与生物农药工作相结合将显着减少化学农药的使用和作物产生抗药性的风险，并将因缺乏化学农药而增加生物多样性。除了这些好处外，该项目还将为英国和中国境外的工业合作伙伴开辟新的商机。