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

用于气候智能型害虫管理的自适应人工智能移动智能解决方案

• 批准号: 10044737
• 金额: $ 6.31万
• 依托单位: SPARKSOFT LIMITED
• 依托单位国家: 英国
• 项目类别: Grant for R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10044737
• 关键词: Adaptive; AI; enabled; Mobile; Intelligence

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.

在英国，各种各样的害虫影响着可耕地作物（如小麦、大麦、油菜籽、土豆）。害虫可对可耕地作物造成重大损害，平均产量损失15-20%，在虫害严重的情况下，这一损失可增加到80%。农药通常用于农作物，以提供保护和限制产量损失。这些应用通常是在保险的基础上进行的（喷洒作为应急措施，以减轻潜在的产量损失），而不是在规定的基础上进行的（在超过损害阈值的水平下，按照正确的害虫识别进行应用）。保险喷剂的应用可以增加昆虫种群的进化压力，导致抗性昆虫种群的发展。对智能系统的需求日益增长，这些系统可以通过智能农药使用来帮助农民实现更可持续的增长，从而提高农场的抵御能力，同时支持更可持续的做法。目前，英国农作物害虫的预防和控制在很大程度上取决于专家的人工检查和对农民或种植者的适当虫害控制措施的建议。然而，个体（种植者主导的）作物有害生物鉴定是有限的，因为准确的有害生物鉴定需要分类学培训。一个流行的解决方案是使用人工智能（AI）技术来自动识别基于图像的害虫。然而，由于作物的多样性和害虫的多样性，这些解决方案在实际应用中准确性和稳健性降低。该项目将研究将上下文和视觉信息与自适应人工智能技术整合到移动解决方案中的技术可行性，该解决方案提供：1)快速检测和量化耕地作物害虫；2)有效预测可接受的有害生物阈值；3)气候智能型病虫害农药相应药效评估。我们将探索优化谢菲尔德大学（UoS）现有的深度学习害虫检测模型PestNet，在识别多种耕地作物害虫方面具有子结构最优运输机制；评估融合现有数据集的图像特征和上下文信息的自适应持续学习技术，以支持气候变化有害生物量化；探讨如何将此与已确定有害生物物种的抗杀虫剂状况的公认有害生物阈值水平相结合，以产生信息丰富的有害生物管理产出。该项目将建立在现有资源和技术的基础上，包括病虫害数据集（3.2K带注释的小麦病虫害图像）和来自Spark-Soft的移动云平台，以及谢菲尔德大学（UoS）的pest net模型。Spark-Soft将与UoS合作管理该项目的开发活动。