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

• 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

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 寻求在精准农业、农业数字化和决策管理支持领域利用马铃薯病害识别和疫情管理的现有研究。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见解，以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，并允许通过开放 API 实现互操作性。这将确保出现一个消费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农作物病害之一。在典型的枯萎病压力季节，农作物保护化学品每年给该行业造成 10-200 亿美元的损失。晚疫病被称为“社区疾病”，因为它能够在适当的天气条件下迅速从田地传播到田地。当天气凉爽潮湿时，无性孢子很容易乘风传播，并能迅速感染邻近的田地。因此，了解该疾病的症状以及发现该疾病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。

项目成果

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

基于深度学习的高分辨率高光谱无人机图像自动检测黄锈病的方法

• DOI: http://dx.10.3390/rs11131554
• 发表时间: 2019
• 期刊: Remote Sensing
• 影响因子: 5
• 作者: Zhang X

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

用于高光谱图像植被识别的生物学可解释两级深度神经网络 (BIT-DNN)

• DOI: http://dx.10.48550/arxiv.2004.08886
• 发表时间: 2020
• 作者: Shi Y

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.

一种快速傅里叶卷积深度神经网络，可从 Sentinel-2 时间序列数据中准确且可解释地辨别小麦黄锈病和氮缺乏症。

• DOI: http://dx.10.3389/fpls.2023.1250844
• 发表时间: 2023
• 期刊: Frontiers in plant science
• 作者: Shi Y

A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-Resolution

• DOI: 10.1109/tgrs.2022.3193441
• 发表时间: 2021-11-16
• 期刊: IEEE Transactions on Geoscience and Remote Sensing
• 影响因子: 8.2
• 作者: Yue Shi;Liangxiu Han;Lianghao Han;Sheng Chang;Tongle Hu;D. Dancey
• 通讯作者: D. Dancey

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: http://dx.10.48550/arxiv.2306.17207
• 发表时间: 2023
• 作者: Shi Y