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

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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.