14TSB_DataExpl Crowd-Sourced Prediction of Plant Pest and Disease Occurrence using Mobile Apps

14TSB_DataExpl 使用移动应用程序对植物病虫害发生进行众包预测

• 批准号: BB/M006980/1
• 负责人: Daniel Kudenko
• 金额: $ 10.68万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM006980%2F1
• 关键词: 14TSB_DataExpl; Crowd; Sourced; Prediction; Plant

Growing Interactive, the industrial lead partner of the project, produces the leading on-line software and apps that gardeners and small-scale farms use to plan the edible crops they grow and achieve increased levels of success. Over a quarter million gardeners and farmers have used their software and apps. They hold a wealth of location-based information on which crops, varieties and quantities gardeners are growing in their location and this year they are extending the recording to include dated observations of pests and diseases on crops (launching May 2014). Event-based journalling has been the most requested new feature for their software and they will be adding social feedback elements to reward reporting.We propose that this data be statistically analysed in conjunction with meteorological information to develop predictive models for pest and disease emergence on crops. By developing advanced map-based visualisations, the vast quantity of crowd-sourced data can be analysed in depth and used to refine predictive models. Meteorological information and weather forecasts can then provide significantly improved pest prediction for growers for the current growing season specific to their location.

该项目的行业领先合作伙伴Growing Interactive生产领先的在线软件和应用程序，园丁和小规模农场使用这些软件和应用程序来规划他们种植的可食用作物，并提高成功率。超过25万园丁和农民使用了他们的软件和应用程序。他们拥有丰富的基于位置的信息，包括园丁在其所在地种植的作物，品种和数量，今年他们正在扩展记录，包括对作物病虫害的日期观察（2014年5月推出）。基于事件的日志一直是他们的软件最需要的新功能，他们将增加社会反馈元素，以奖励reporting.We建议，这些数据进行统计分析，结合气象信息，开发预测模型的病虫害出现的农作物。通过开发先进的基于地图的可视化，可以深入分析大量的众包数据，并用于改进预测模型。然后，气象信息和天气预报可以为种植者提供针对其所在地的当前生长季节的显著改进的害虫预测。

项目成果

Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach

• DOI: 10.1016/j.ecolind.2017.10.032
• 发表时间: 2018-05-01
• 期刊: ECOLOGICAL INDICATORS
• 影响因子: 6.9
• 作者: Holloway, Paul;Kudenko, Daniel;Bell, James R.
• 通讯作者: Bell, James R.