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

了解土壤健康和农业管理在促进土壤可持续性方面的作用近年来受到越来越多的关注，特别是自从“改善土壤健康”被纳入英国政府的25年环境计划以来。英国脱欧后可能会为改善环境提供农业补贴，因此现在需要开发监测系统。蚯蚓可以被描述为土壤健康的象征，推动养分循环和水分渗透过程-如果土壤中有丰富的蚯蚓种群，那么土壤动物的其余部分可能也会健康，土壤化学和土壤结构也会健康。传统上，由于评估人员的能力，人口监测是费力的，可能是不准确的，公民科学监测计划已经过试验，以降低成本，但尚未在全国范围内推广。利用计算机学习作为一种工具，在现场规模的高通量识别的土壤丰度可以在英国的农田，提供一个目前的评估土壤活动。由于蚯蚓挖洞减少了水的流失，改善了土壤的孔隙度，这种方法提供了一种低成本、快速的监测评估工具，可以提供一种“生物健康评估”，为农民提供信息和教育，并改善农业管理。该提案旨在开发一种深度学习算法工具，以高吞吐量原位检测和计数重复播放。如果成功，基于这种生物图像分析的软件可以通过智能手机应用程序或无人驾驶车辆部署，从而在全国范围内对蚯蚓进行实地监测。迄今为止，已经开发了许多应用程序来测量土壤/土壤健康，但没有一个将计算机深度学习的物体识别与蚯蚓活动结合起来，这是一个明显的研究空白，本提案旨在填补这一空白。联合收割机。

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.