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UK-China Agritech Challenge: CropDoc - Precision Crop Disease Management for Farm Productivity and Food Security

中英农业科技挑战赛：CropDoc - 精准作物病害管理，提高农业生产力和粮食安全

基本信息

批准号：
BB/S020969/1
负责人：
Liangxiu Han
金额：
$ 57.24万
依托单位：
Manchester Metropolitan University
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%2FS020969%2F1
关键词：
UK China Agritech Challenge CropDoc

项目摘要

CropDoc seeks to exploit existing research on Potato disease identification and outbreak management in the domain of precision agriculture, agriculture digitisation & decision management support. It will harness cutting-edge technologies (i.e. IoT, mobile devices, crowd sourced data, big data analytics and cloud computing). It will build a decision support system that generates insight from multiple data collected from remote sensing above the fields and IoT ground sensing within the fields for monitoring & prediction of disease in real time. CropDoc will base its data service and analytics platform on open standards and will allow interoperability through open APIs. This will ensure an end-platform ecosystem can emerge that consumes the data & analytics service, allowing farmers to use their platform of choice, while allowing central authorities to identify and manage sector outbreaks. The initial focus will be on potato late blight disease, one of the most devastating crop diseases in China. In a typical blight pressure season crop protection chemicals cost the industry an estimated $10-20bn per annum. Late blight has been referred to as a 'community disease', due to its ability to spread rapidly from field to field under the right weather conditions. Asexual spores travel easily on the wind when the weather is cool and moist, and can rapidly infect neighbouring fields. As such, understanding the symptoms of the disease and what to do when it is detected are essential to preventing an outbreak from rapidly turning into an epidemic.

CropDoc旨在利用精准农业、农业数字化和决策管理支持领域有关马铃薯疾病识别和爆发管理的现有研究。它将利用尖端技术（即物联网、移动的设备、众包数据、大数据分析和云计算）。它将建立一个决策支持系统，该系统从田间遥感和田间物联网地面传感收集的多个数据中产生洞察力，用于真实的疾病监测和预测。CropDoc将基于开放标准的数据服务和分析平台，并通过开放API实现互操作性。这将确保终端平台生态系统能够出现，消费数据和分析服务，允许农民使用他们选择的平台，同时允许中央当局识别和管理部门疫情。最初的重点将是马铃薯晚疫病，这是中国最具破坏性的作物病害之一。在一个典型的枯萎病压力季节，作物保护化学品估计每年花费该行业100亿至200亿美元。晚疫病被称为“群体病”，因为它能够在适当的天气条件下从一个田地迅速传播到另一个田地。当天气凉爽潮湿时，无性孢子很容易随风传播，并能迅速感染邻近的田地。因此，了解疾病的症状以及在发现时采取什么措施对于防止疫情迅速演变为流行病至关重要。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Biologically Interpretable Two-Stage Deep Neural Network (BIT-DNN) for Vegetation Recognition From Hyperspectral Imagery

DOI：
10.1109/tgrs.2021.3058782
发表时间：
2021-02
期刊：
IEEE Transactions on Geoscience and Remote Sensing
影响因子：
8.2
作者：
Yue Shi;Liangxiu Han;Wenjiang Huang;Sheng Chang;Yingying Dong;D. Dancey;Lianghao Han
通讯作者：
Yue Shi;Liangxiu Han;Wenjiang Huang;Sheng Chang;Yingying Dong;D. Dancey;Lianghao Han

Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

DOI：
10.3390/rs14020396
发表时间：
2022-01
期刊：
Remote. Sens.
影响因子：
0
作者：
Yue Shi;Liangxiu Han;Anthony Kleerekoper;Sheng Chang;Tongle Hu
通讯作者：
Yue Shi;Liangxiu Han;Anthony Kleerekoper;Sheng Chang;Tongle Hu

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

DOI：
10.3390/rs11131554
发表时间：
2019-06
期刊：
Remote. Sens.
影响因子：
0
作者：
Xin Zhang;Liangxiu Han;Yingying Dong;Yue Shi;Wenjiang Huang;Lianghao Han;P. González-Moreno;
通讯作者：
Xin Zhang;Liangxiu Han;Yingying Dong;Yue Shi;Wenjiang Huang;Lianghao Han;P. González-Moreno;

A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data

快速傅立叶卷积深度神经网络，用于根据 Sentinel-2 时间序列数据准确且可解释地判别小麦黄锈病和氮缺乏症