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. Currently, farmers are advised to follow economic thresholds and to apply management interventions when thresholds are exceeded. Generally, thresholds are defined as the level of pest infestation above which it is expected the crop will suffer economic damage. 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. Effective IPM deployment is dependent on accurate pest identification and quantification, accurate pest identification and quantification, placing this pest information into a crop tolerance threshold context, and deploying sustainable and economically sound pest control strategies. 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; and insecticide resistance information for key pests is spatially-limited and primarily provided on a national basis. The central barrier for IPM uptake is lack of grower confidence in their ability to identify a pest. In a recent project we developed an early-stage solution to this problem by building an AI-driven pest-detection model to identify insect pests in wheat crops (Innovate project 10002902). Here, we propose to build on the success of this project by expanding mobile visual intelligence with ChatGPT technology into an improved pest management solution that: * Offers rapid detection and quantification of crop pests using mobile devices. * Places pest quantification into context of regionally relevant pest tolerance thresholds. * Provides estimation of economic thresholds and useful advice on crop pest management. To achieve this we will expand the pest detection model to above-ground pests of rapeseed and potato: cabbage aphid, peach-potato aphid, potato aphid, and the cabbage stem flea beetle, and test and validate thresholds for a subset of these pests. Our main output will be a smart-app that provides pest detection support, highlights the current threshold for the identified pest, and provides information on estimation of economic thresholds and useful advice on crop pest management. AI-model development will be led by The University of Sheffield; provision of pest management advice will be led by ADAS; and the development of the smart-app user-interface will be led by Mutus Tech Ltd.