UK-China Agritech Challenge: CropDoc - Precision Crop Disease Management for Farm Productivity and Food Security
UK-China Agritech Challenge: CropDoc - Precision Crop Disease Management for Farm Productivity and Food Security
批准号:
BB/S020969/1
负责人:
Liangxiu Han
金额:
$57.24万
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
项目状态:
已结题
起止时间:
2019 至 --
中文摘要
CropDoc寻求在精准农业、农业数字化和决策管理支持领域利用现有的马铃薯病害识别和暴发管理研究。它将利用尖端技术(即物联网、移动设备、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,从田间遥感和田间物联网地面传感收集的多个数据中生成洞察力,用于实时监测和预测疾病。CropDoc将其数据服务和分析平台建立在开放标准的基础上,并将通过开放API实现互操作性。这将确保出现一个使用数据和分析服务的终端平台生态系统,允许农民使用他们选择的平台,同时允许中央部门识别和管理行业疫情。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,作物保护化学品每年估计要花费该行业100至200亿美元。晚疫病一直被称为“社区疾病”,因为它能够在适当的天气条件下在田地之间迅速传播。当天气凉爽潮湿时,无性孢子很容易在风中传播,并能迅速感染邻近的田地。因此,了解疾病的症状以及在检测到它时应采取的措施对于防止疫情迅速演变为流行病至关重要。
英文摘要
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.
期刊论文(10)
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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
DOI:
10.3390/rs14020396
发表时间:
2022-01
期刊:
Remote. Sens.
影响因子:
--
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
DOI:
10.3390/rs11131554
发表时间:
2019-06
期刊:
Remote. Sens.
影响因子:
--
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
DOI:
10.48550/arxiv.2306.17207
发表时间:
2023
期刊:
影响因子:
--
作者:
[Shi Y]
通讯作者:
Shi Y
DOI:
10.3389/fpls.2023.1250844
发表时间:
2023
期刊:
Frontiers in plant science
影响因子:
5.6
作者:
[]
通讯作者:
共 6 条
Synergising Process-Based and Machine Learning Models for Accurate and Explainable Crop Yield Prediction along with Environmental Impact Assessment
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批准号:BB/Y513763/1
-
项目类别:Research Grant
-
资助金额:$31.02万
-
财政年份:2024
-
负责人:Liangxiu Han
-
依托单位:
EYE-SCREEN-4-DPN: Development of an innovative Intelligent EYE imaging solution for SCREENing of Diabetic Peripheral Neuropathy
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依托单位:
EPIC: An automated diagnostic tool for Potato Late Blight disease detection from images
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项目类别:Research Grant
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资助金额:$8.23万
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负责人:Liangxiu Han
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依托单位:
国内基金
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