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EPIC: An automated diagnostic tool for Potato Late Blight disease detection from images

EPIC：一种从图像检测马铃薯晚疫病的自动化诊断工具

基本信息

批准号：
BB/R019983/1
负责人：
Liangxiu Han
金额：
$ 8.23万
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：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FR019983%2F1
关键词：
EPIC automated diagnostic tool Potato

项目摘要

The yields of crop plants are deleteriously affected by various diseases. It is estimated that almost 25% of worldwide crops are lost to diseases, which may cause devastating economical, social and ecological losses. In China, as the fourth important food crop, yield losses from potato late blight diseases can vary from 20%-40% in common years. In severe cases, the yield loss may reach 50%-100%. The estimated yearly economic losses due to this disease are around $5 billion in China. Early accurate detection and identification of crop diseases plays an important role in effectively controlling and preventing diseases for sustainable agriculture and food security.In our previous funded projects, we have developed an innovative automated machine vision system for efficient crop disease diagnosis from images, which have proven the technical feasibility of using advanced image processing, machine learning, mobile and cloud computing approaches. This project will take it forward and develop a near-market product ready for commercialisation, which can provide more accurate real-time information for crop disease surveillance. The tool can run on mobile devices. Farmers with basic training can perform disease diagnosis immediately. Compared to the current practices using human visual observation (which is labour intensive, costly and error-prone), this machine vision system can dramatically speed up diagnosis, and give growers more accurate information on which to base their disease control strategies and stop crop yields from being reduced by infection. This technology can overcome lack of expertise, help make a significant impact on agricultural productivity and farmer incomes, ensuring food security, and deliver highly cost-effective, long-term economic and social impact in China.To achieve actual impact and demonstrable benefits in China, this project will work closely with Chinese partners from academia, industry and farmers including: the project partner (Hebei Agriculture University (HEBAU)), and end users (Beijing Mengbangda Biotechnology Co. Ltd (BMB) and Guyuan County Potato Association (GCPA)). They will provide support in gathering the field data, setting up trial systems and domain knowledge input from plant pathologists for local agriculture and fine tuning the systems in the fields, as well as potential commercialization of this technology in China (Hebei province initially). The project focuses on three stages of translational/user engagement: 1) System requirement gathering from users; 2) System evaluation with input from users; 3) Potential impact and commercial exploitation with end users. The tool will be initially deployed in the real fields provided by end users (BMB and GCPA) in Hebei province at the end of project. This will help protect potato-planting area of over 380k MU initially from the disease infection with reduced annual costs on fungicide usage and damages to environment. Other translational activities include organization of workshops, conference attendance and paper publications, which will be used for dissemination and engagement with a wide range of user groups on a large-scale.This project will not only develop a near-market product but will also generate a significantly measurable impact, promote long-term sustainable growth, economic development and welfare in China and beyond.

农作物的产量受到各种病害的严重影响。据估计，全球近25%的作物因疾病而损失，这可能造成毁灭性的经济，社会和生态损失。在我国，马铃薯作为第四大粮食作物，一般年份晚疫病造成的产量损失可达20%-40%。严重时，产量损失可达50%-100%。据估计，中国每年因这种疾病造成的经济损失约为50亿美元。作物病害的早期准确检测和识别对于有效控制和预防病害，实现农业可持续发展和粮食安全具有重要作用。在我们之前的资助项目中，我们开发了一种创新的自动化机器视觉系统，用于从图像中高效诊断作物病害，该系统已经证明了使用先进的图像处理，机器学习，移动的和云计算方法的技术可行性。该项目将继续推进，并开发一种接近市场的产品，可用于商业化，为作物病害监测提供更准确的实时信息。该工具可以在移动的设备上运行。受过基本培训的农民可以立即进行疾病诊断。与目前使用人工视觉观察的做法（劳动密集型，昂贵且容易出错）相比，这种机器视觉系统可以大大加快诊断速度，并为种植者提供更准确的信息，以制定疾病控制策略并阻止作物产量因感染而减少。这项技术可以克服专业知识的缺乏，有助于对农业生产力和农民收入产生重大影响，确保粮食安全，并在中国产生高成本效益的长期经济和社会影响。为了在中国实现实际影响和可证明的效益，该项目将与来自学术界，工业界和农民的中国合作伙伴密切合作，包括：项目合作伙伴（河北农业大学（HEBAU））和最终用户（北京蒙邦达生物技术有限公司（BMB）和沽源县马铃薯协会（GCPA））。他们将支持收集田间数据，建立试验系统和植物病理学家为当地农业提供的领域知识输入，并在田间微调系统，以及该技术在中国（最初是河北省）的潜在商业化。该项目侧重于翻译/用户参与的三个阶段：1）从用户那里收集系统需求; 2）根据用户的输入进行系统评估; 3）与最终用户一起进行潜在影响和商业开发。该工具将在项目结束时首先部署在河北省最终用户（BMB和GCPA）提供的真实的字段中。这将有助于保护超过38万亩的马铃薯种植面积最初免受疾病感染，减少每年的杀菌剂使用成本和对环境的破坏。其他翻译活动包括组织研讨会、出席会议和纸质出版物，这些活动将用于大规模的传播和接触广泛的用户群体。该项目不仅将开发接近市场的产品，还将产生显著可衡量的影响，促进中国和其他地区的长期可持续增长、经济发展和福利。

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