Adaptive AI-enabled Mobile Intelligence Solutions for Climate-smart Pest Management

In the UK a diverse range of pests affect arable crops (e.g., wheat, barley, rapeseed, potatoes). Insect pests can cause significant damage to arable crops, with average yield of losses of 15-20%, with this increasing to 80% under high levels of infestation. Pesticides are often applied to crops to provide protection and to limit yield losses. These applications are often done on an insurance basis (a spray is applied as contingency to mitigate potential yield loss) rather than a prescriptive basis (application follows correct pest identification with levels exceeding damage thresholds). The application of insurance sprays can increase the evolutionary pressure on insect populations, leading to the development of resistant insect populations. There is increasing demand for intelligent systems that can help farmers grow more sustainably through smart pesticide use to improve farm resilience while supporting more sustainable practices.

Currently, the prevention and control of UK crops pests heavily reply on experts' manual inspections and recommendations to farmers or growers on appropriate pest control measures. However, individual (grower-led) identification of crop pests is limited as accurate pest identification requires taxonomic training. A popular solution for this is the use of artificial intelligence (AI) techniques for automated, image-based identification of pests. However, these solutions suffer from reduced accuracy and robustness in real-world applications due to multiplicity of crops and variety of pests.

This project will investigate the technical feasibility of integrating contextual and visual information with adaptive AI technique into a mobile solution that offers: 1) rapid detection and quantification of arable crops pests; 2) efficient forecasting of accepted pest thresholds; 3) estimation of the corresponding efficacy of a pesticide for climate-smart pest control. We will explore optimising existing deep learning pest detection model PestNet by the University of Sheffield (UoS) with substructural optimal transport mechanisms in recognising multiple arable crops pests; evaluate adaptive continuous learning techniques for fusing image features and contextual information from existing datasets for supporting climate-change pest quantification; explore how this can be combined with accepted pest threshold levels on the pesticide resistance status of the identified pest species to produce an informative pest management output.

The project will build on existing resources and technologies including pest data set (3.2K annotated wheat pest images) and mobile-cloud platforms from Spark-Soft, and PestNet model at the University of Sheffield (UoS). Spark-Soft will manage the development activity of this project in collaboration with UoS.