Runoff: Remote worming - developing computer learning for high throughput identification of earthworm populations as an indicator of soil health

径流：远程蠕虫 - 开发计算机学习以高通量识别蚯蚓种群作为土壤健康的指标

• 批准号: ST/V000357/1
• 负责人: Felicity Crotty
• 金额: $ 1.48万
• 依托单位: Royal Agricultural University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FV000357%2F1
• 关键词: Runoff; Remote; worming; developing; computer

Understanding soil health and the effect agricultural management has in promoting the sustainability of the soil has increased in scrutiny in recent years particularly since "improving soil health" was included in the UK Government's 25 year plan for the environment. Post-Brexit farming subsidies are likely to be given for environmental improvement, therefore there is a need to develop monitoring systems now. Earthworms can be described as the emblem of a soil health, driving nutrient cycling and water infiltration processes - if there is an abundant earthworm population within the soil, the likelihood is the rest of the soil fauna will also be healthy as will the soil chemistry and soil structure. Traditionally earthworm population monitoring is laborious and can be inaccurate, due to the ability of the assessor, citizen science monitoring programs have been trialled to reduce costs, but have not been extended across the country. Utilising computer learning as a tool for high throughput identification of earthworm abundance at a field-scale could be implemented across farmland within the UK, to provide a current assessment of earthworm activity. As earthworms burrowing reduces water runoff and improves soil porosity, this method provides a low cost, fast monitoring assessment tool that would provide a "biological health assessment" that could inform and educate farmers and lead to improvements in agricultural management. This proposal aims to develop a deep learning algorithm tool to detect and count earthworm casts in-situ at high-throughput. If successful, software based on this bioimage analysis could be deployed via smartphone app or unmanned vehicle, leading to monitoring of earthworms nationally at the field-scale. To date there have been many apps developed to measure soil / soil health, but none combine computer deep-learning for object recognition with earthworm activity, this is a clear research gap, that this proposal aims to fill.

了解土壤健康以及农业管理对促进土壤可持续性的影响近年来的审查有所增加，特别是自从英国政府的25年环境计划中包括“改善土壤健康”以来。脱欧后的农业补贴可能会用于环境改善，因此现在有必要开发监测系统。 earth可以描述为土壤健康的象征，驱动养分循环和水渗透过程 - 如果土壤内有大量的earth虫种群，则可能是土壤动物动物的其余可能性，土壤化学和土壤化学也将是健康的。土壤结构。传统上，由于评估人员的能力，公民科学监测计划已经过试验以降低成本，但在全国范围内尚未扩展。可以利用计算机学习作为在田间规模的高吞吐量识别的工具，可以在英国境内的农田中实施，以提供当前对earth活动的评估。 earth挖洞会减少水径流并改善土壤孔隙度，该方法提供了低成本，快速监测的评估工具，该工具将提供“生物健康评估”，可以为农民提供信息并教育农民并改善农业管理。该提案旨在开发一种深度学习算法工具，以高通量在原位中检测和计算earth铸造。如果成功的话，可以通过智能手机应用程序或无人使用的车辆部署基于此生物图像分析的软件，从而在现场尺度上对全国范围内的earth进行监测。迄今为止，已经开发了许多用于衡量土壤 /土壤健康的应用程序，但是没有一个应用程序结合了计算机深入学习，以识别物体识别earth活动，这是一个明显的研究差距，该提案旨在填补。