Development of an advanced synthetic data modelling engine capable of automatically producing high volumes of variable complex crop scene datasets at >10% of real-world costs within days rather than months/years to train agricultural robots

Agricultural robots require effectively trained AI systems to carry out functions effectively. The agricultural sector is one of the most difficult in which to train AI systems to interpret agricultural scenes due to multiple layers of complexity:

* **Plant/weed species:** huge species variances and multiple species both difficult to distinguish at early growth stages
* **Occlusion:** In complex crop scenes many crop and weed plants overlap in a complex manner
* **Physical changes:** Effects of pests/diseases, leaf/crop deformities and soil changes
* **Different presentations:** Camera angles, scene lighting and backgrounds create variabilities and translucency effects
* **Annotation:** Annotation of images at pixel level is almost impossible for humans to do accurately and at volume

One of the most difficult and economically damaging problems for UK farmers is blackgrass, which threatens the viability of wheat crops. Blackgrass is difficult for an agricultural robot to detect/distinguish at the early stage which is required for effective elimination treatments as this requires a significant, varied, dataset which could take years to obtain. Such a robust AI solution does not exist today.

During this project, we will develop an advanced synthetic image modelling engine capable of automatically producing high volumes of variable complex crop scene datasets at <10% of real-world costs. These can be used to effectively train AI solutions within days rather than months/years. This will enable agricultural robots to robustly detect blackgrass.

Blackgrass is the first use case and further development within the project will enable other species to be classified in a wide range of conditions.