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.

该项目的工业主要合作伙伴的互动互动越来越多，它生产了园丁和小型农场用来计划其种植的食用作物并取得成功水平的领先的在线软件和应用程序。超过2500万园丁和农民使用了他们的软件和应用程序。他们拥有大量基于位置的信息，园丁在其位置正在增长，今年他们将录音扩展到了农作物上的害虫和疾病的过时观察（2014年5月启动）。基于事件的期刊是其软件最需要的新功能，他们将添加社交反馈元素以奖励报告。我们建议将这些数据与气象学信息一起统计分析，以开发有害生物和疾病在农作物上出现的预测模型。通过开发基于地图的高级可视化，可以深入分析大量的人群数据，并用于完善预测模型。然后，气象信息和天气预报可以为当前生长季节特定地点的生长季节提供显着改善的虫害预测。

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.