A robot-enabled, data-driven machine vision tool for nitrogen diagnosis of arable soils

Abstract

Increasing crop productivity with reduced inputs and lower impacts on the environment is a major challenge for global food production. Among all farmer-controlled input factors, Nitrogen (N) has the second-largest impact on crop growth after water and arable soils are a predominant source of N in many cropping systems. Optimal N fertilisation can increase crop production and enhance soil fertility. On the other hand, high N inputs are costly for farmers and result in reductions in plant biodiversity, pollution of natural ecosystems and increases in emissions of the potent greenhouse gas, nitrous oxide. Current spend on fertilisers by UK farmers is £1.345bn. At present excessively high N fertiliser rates are used by farmers because they are not aware of the areas of land where N is excessive, optimal or deficient. Accurate detection of soil N is crucial for the economic and environmental sustainability of cropping systems. The current practices in determining soil N is costly, labour intensive and time consuming and so high N inputs are common.

For the first time, driven by the end user needs, this innovative interdisciplinary project between academia, industry and farmers will co-develop a cost-effective, non-destructive, robot enabled, data driven, machine vision solution by harnessing disruptive technologies (Robotics, AI/Computer Vision/Big Data Analytics), agricultural science to automatically detect nitrogen levels of arable soils, capable of 1) automated data collection using a mobile robot with 3D imaging sensing; 2) automated intelligent diagnosis of both crop N and soil N based on AI/Computer Vision/big data analytics which derives N using the crop and/or cover crop(s) as a quantitative bioindicator of soil N values. This utilises known plant responses to N enrichment with models parameterised by precise relationships derived in this study. The integration of sensor systems and machine vision/data analytics with autonomous robotic platforms offer significant opportunities for new measurements and machine vision-based tasks in food production that would otherwise be unobtainable and unachievable. This precision agriculture solution will transform food production by reducing N inputs, increasing farm profitability and contribute to net-zero emission by reducing emissions of nitrous oxide through offering early detection of both crop N and soil N, providing accurate information on nitrogen N use efficiency and soil quality assessment. It will position UK precision agriculture technologies at the forefront of new industries and drive economic growth in UK.

Lead Participant

Project Cost

Grant Offer

Manchester Metropolitan University £104,877 £ 104,877
 

Participant

Gmv Innovating Solutions Limited, London £124,796 £ 62,398
Royal Botanic Gardens Kew, United Kingdom £19,941 £ 19,941

Publications

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