Nitrogen fertilizer enables us to grow crops with higher yields supporting half of the World's population. Today, fertiliser production consumes approximately 1.2% of the world's energy excluding energy cost of transportation, storage and application of the fertilizer. There is no efficient way to measure soil N leading to fertilizer overuse (sometimes by 200-300% of what is required). Excess nitrogen can also lead to poor crop health and promotes diseases. This in turn leads to the need for pesticides and fungicides. In addition, excess nitrogen can cause crops to lodge before harvest, leading to lower yields and quality.

Accurate precision fertilisation based on in-situ measurement drastically reduces the amount of fertiliser that is applied, reducing energy required to manufacture, distribute, store and apply fertilizer and additionally improve, crop yields and health, whilst reducing carbon emissions and environmental pollution - contributing to the UK's net zero targets.

A cost efficient tool that farmers can use to accurately measure soil nitrogen is needed. The solution proposed is an autonomous robot that can measure soil quality in-situ. Low capital cost and full autonomy is required to allow for return on investment and cheap operation. Additionally the robot needs to be fully electric with option of re-charging from solar or other renewable power in-field. We have tested mapping capability of the robot and demonstrated functionality in grassland.

Currently the robot, autonomy instrumentation is already finalised and the autonomy software has been tested in certain environment but requires further adjustment for some crops. This project focuses on the mapping camera and soil sensor and the AI models required to create accurate Nitrogen maps and calibrate the sensors in-situ. The AI models will be created in collaboration with Manchester Metropolitan University (MMU).

The project specifically targets the export market looking at high value crops in South East Asia such as Coffee, Mulberry (silk), tea and cotton. Coffee in particular requires optimisation of fertilisation to make production sustainable.

The ethical consideration of automation and AI is ownership. We believe these tools need to be affordable enough so that end-users can invest and get full ownership of the equipment. It is our social responsible to make autonomous equipment affordable.