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. In an initial project we developed an early-stage solution to this problem by integrating visual intelligence with ChatGPT technology into a mobile solution to identify insect pests in crops (InnovateUK project 10123768). The outcome could be further improved to be more successful when given additional funding to support 8 months, with benefits below: Here, we will improve our PestGPT solution through optimising pest detection models and integrating knowledge guided ChatGPT to manage multiple pests of above-ground crops. The output will be an enhanced PestGPT solution that offers: * Offers rapid detection and quantification of over 30 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 control. To achieve this we will expand the pest detection model to above-ground crops (over 10) and pests (over 30), 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.

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.

UK agriculture is responsible for 10% of the UK's emissions, where the 967K tonnes of Nitrate fertilisers the UK uses annually release around 5.4 million tonnes of greenhouse gases (GHG). Farmers are advised to follow AHDB guidance to plan their fertiliser practices for mitigating GHG emissions. As rising cost of fertilisers and restrictions on fertiliser use, growers are looking towards more sustainable fertiliser practices (SFP). There is increasing demand for innovative approaches that can help farmers grow sustainably through climate-smart fertiliser practice to improve farm resilience. Effective SFP is dependent on appropriate selection of fertiliser types and application timing, confidence in fertiliser application rates, and accurate quantification of its impacts on soil health. Several classic SFP solutions are available, but limitations restrict their usability: * Organic fertilisers contribute to improving soil health by enhancing soil structure, moisture retention, and microbial activity, but suffer from low release of nutrients and increasing costs. * Control-release fertilisers provide a sustained source of nutrients for crops with long-growth cycles and slow nutrient release rates, but have relatively higher initial cost and limited application flexibility. * Nutrient management planning enables optimising the use of fertilisers and other nutrient sources, but require rich technical knowledge and expertise. Implementing these solutions can be highly costly, complex and inflexible, where it can be challenging for farmers with limited resources or lacking access to necessary tools. In a recent InnovateUK project ( 107462), we developed a sustainable fertiliser management platform _'ParallelFarm'_ with adaptive AI techniques for simulating and predicting fertiliser practices_._ Here, we propose to build on the success of this project by evaluating and optimising AI-enabled fertiliser practices in wheat production that offers: * reducing crop growth stage and environmental conditions to provide climate-smart fertiliser recommendation. * accessing and optimising use efficiency of advance fertiliser with urease or nitrification. * quantification of soil GHG flex response to fertiliser applications It will build on existing resources in the consortium, including: ParallelFarm platform (Mutus-Tech), AI enabled fertiliser prediction model (UoS), agronomic and fertiliser expertise (HAU), and farms from Velcourt. This new AI-enabled climate-smart fertiliser practice solution will improve farm resilience, reduce unnecessary fertiliser use, and improve farming productivity. The technology will help tackle wider challenges such as reducing growing costs for farmers and the environmental impacts of fertiliser use. Additionally, it will provide a soil GHG accessing training tool for farmers/advisors. Ultimately, it will improve farm productivity and profits, and stimulate growth of the UK market.

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.

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. To effectively deploy IPM practices three components are required: 1) accurate identification of the pest(s) present; 2) accurate information on thresholds and their efficacy; 3) information on insecticide resistance of the local/regional pest population. 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 the AI-driven pest-detection model to pests of other arable crops and by integrating more information into the end-user output in order to address the other barriers to IPM uptake. To achieve this we will expand the pest detection model to above-ground pests of rapeseed and potato, integrate region-specific insecticide resistance status for key pests: 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 the insecticide resistant status of regional pest populations. AI-model development will be led by The University of Sheffield; provision of pest management advice and threshold testing will be led by ADAS; insecticide resistance testing will be led by The University of Liverpool; and the development of the smart-app user-interface will be led by Mutus Tech Ltd.

As the world's largest consumer of agricultural chemicals, China has used more than 30 percent of global pesticides on only 9 percent of the world's crop land. Pesticides are often applied to crops to provide protection against pest damage and to limit yield losses in China. These applications are often done on an insurance basis rather than a prescriptive basis because pest abundance is high. With sustainable crop protection becoming more important to achieve the "Net-zero emission" plan in China, there is increasing demand for new pest management tools that can help Chinese farmers grow more sustainably with fewer chemical inputs and reduced soil erosion. This project will explore the feasibility of utilising mobile intelligence techniques as a cost-effective farmer-centred pest management solution with improved economic benefits and environmental sustainability in China. We will rely on our existing deep learning based mobile wheat pest recognition technique developed from an Innovate-UK project, in partnership with the University of Sheffield and China Academy of Sciences (CAS). The technique offers: rapid detection and effective quantification of wheat pests; places pest quantification into context of regionally relevant pest tolerance thresholds; determines whether a pesticide application is advised to use. This project will examine the acceptability of using mobile pest management apps by Chinese smallholder farmers and farmer cooperatives via questionnaires and interviews, analysis the perceived impact of the apps in supporting sustainable agricultural development in China via comprehensive literature reviews, and predict the potential long-term economic benefits of using above technique in China. Through existing collaboration with CAS, we will engage with 4 smallholder farmers and 2 farmer cooperatives at Anhui Province in China. This project will deliver a comprehensive analysis and evaluation report discussing above technique for sustainable pest management in China in terms of user acceptability, environment impacts and business return. This report should include: 1) Farmer requirement identification and their acceptance results analysis of mobile apps usage in sustainable pest management in China; 2) Analysis the strong and weak points of the mobile pest management solution using agronomic, environmental, and social-economic criteria; 3) potential open-source business model that provides basic mobile applications to small-size growers and public for free. 4) The business-to-business approach, which sells professional software licenses, services and professional training to middle-size growers and agronomists in China.

Sustainable management of UK wheat pests and maintenance of soil health have become a high-priority agricultural issue in the UK. This project will investigate the technical feasibility of integrating visual and contextual information with advanced data fusion techniques into a mobile pest management solution that offers: rapid detection and quantification of wheat pest by mobile devices; efficient forecasting of accepted pest thresholds for sustainable management; estimation of the corresponding efficacy of a pesticide for pest control. The project will be led by University of Sheffield, and build on existing technologies, data resources and platforms from previous projects within the consortium.