description
- Worldwide, crops are threatened by invertebrate pests which cause feeding damage and transmit plant viruses. High levels of infestation can cause up to 80% yield loss. 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. As restrictions on insecticide use increase and a greater number of insecticide resistant pest populations emerge, growers are looking towards more sustainable integrated pest management (IPM) practices. However, there are numerous barriers that restrict the uptake of IPM principles: Accurate identification of invertebrate pests is difficult and requires taxonomic training, a skill that growers often lack; current thresholds have received little testing and validation under field conditions, limiting grower confidence, etc. In an initial project we developed an early-stage solution to this problem by building an AI-driven mobile pest-detection solution to identify insect pests in wheat crops (Innovate project 10002902). The outcome could be further improved to be more successful with benefits below: * Expanding wheat pest detection model to above-group crops like potato and rapeseed. * Improving recognition accuracy and efficiency of models running in mobile phones with optimisation mechanisms. * Evaluating and validating the accepted pest thresholds for the focal crop pests. Here, we will propose an follow-on project that improves our AI-driven mobile pest management solution through optimising deep learning detection models with knowledge distillation technique to pests of other arable crops. The output will be an enhanced MPM solution that offers: 1) rapid detection and accurate quantification of foliar pests using mobile devices; 2) placing pest quantification into context of region-specific pest tolerance thresholds; 3) Providing estimation of economic thresholds and advice on pest control. It will build on existing resources in the consortium, including: 10K wheat pest images (Mutus-Tech), a world-leading pest detection model (PestNet; UoS), agronomic and pest management expertise (ADAS). This new solution will improve farm resilience, reduce unnecessary insecticide use, and improve pest control. The technology will also reduce growing costs for farmers. Ultimately, it will improve farm productivity and profits, and stimulate growth of the UK market.