Winter wheat is one of the most important UK crops but has an estimated 20% of potential lose every year due to pests. While pesticides are often applied to crops protection, their uncontrolled application can cause soil erosion and contamination. Sustainable management of pest is reliant on: accurate identification of the pest present, knowledge of the levels of pest damage that can be tolerated, and effective pest management solutions for maintaining soil health. Currently, there is no integrated in-field solution for sustainable management of wheat pest in the UK.
This project investigates the technical feasibility of integrating visual and contextual information with advanced data fusion techniques into a mobile wheat pest management solution that offers:
1) rapid detection and quantification of wheat pest by mobile devices;
2) efficient forecasting of accepted pest thresholds for sustainable management;
3) estimation of the corresponding efficacy of a pesticide for pest control.
It will explore data fusion of mobile image and contextual information from existing datasets by designing and optimising advanced broad learning techniques, and further study the accepted pest thresholds and its corresponding pesticide efficacy usage. The project builds on existing technologies, data resources and platforms from previous projects within consortium.