Real-time vision-based spot spraying development for high efficiency and precision weed management

基于实时视觉的点喷开发，实现高效、精准的杂草管理

• 批准号: 2457960
• 金额: --
• 依托单位: University of Lincoln
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2457960
• 关键词: Real; time; vision; based; spot

Weeds are typically controlled by spraying chemicals uniformly across the whole field. However, the overuse of chemicals in this approach has increased the cost of crop protection and posed negative impacts on the environment and food security, which is a hindrance to sustainable agriculture development. Spot spraying, as a spatially variable weed management strategy, targets only weed species in fields to minimize the use of chemicals. Commercially available technologies based on sensing of vegetation optical properties are typically constrained by detecting weeds on a soil background (i.e. greenness detection in a bare soil background) and are not suitable to detect weeds among a growing crop. A vision-based spot spraying system enables discrimination between vegetation species. One of the key components for the vision-based spot spraying development is to build a reliable and robust weed/crop discrimination model. Traditionally, the development of a vision-based weed/crop discrimination model is highly relying on image analysis with prior knowledge of the defined colour, texture and morphology features between weed and crop. But this might fail to generalise over different crop fields with multiple weed species. The recent technology advancements in machine learning and computer vision have provided new opportunities to develop a robust and reliable vision-based weed/crop discrimination model under unstructured field conditions. Currently, training a deep machine learning model require large numbers of labelled images. In this project, in order to remedy the manual labelling task and speed up the development process, a novel pipeline is proposed to generate realistic synthetic images based on the combination of conventional image analysis and generative adversarial networks (GANs). Furthermore, the project will also focus on dealing with the current bottlenecks of vision-based spot spraying development with regards to discrimination model generalization ability and spraying efficiency. Specifically, the objectives of this project are to (1) build two image data libraries (weed and crop) that allow fast annotated synthetic images generation via the developed generation pipeline; to (2) develop a robust and lightweight deep learning-based detection model based on large numbers of synthetic images and to (3) integrate the developed weed detection system into a spray boom for spot spraying and demonstrate its feasibility for high efficiency spot spraying (driving speed > 8 km/h) under field conditions. The demonstration will be displayed at a sugar beet field. The evaluation will be made in terms of spraying accuracy and efficiency.

杂草通常是通过在整个田地均匀喷洒化学药品来控制的。然而，这种方法中化学品的过度使用增加了作物保护的成本，对环境和粮食安全造成了负面影响，阻碍了农业的可持续发展。现场喷洒作为一种空间可变的杂草管理策略，只针对田间杂草品种，以尽量减少化学品的使用。商业上可用的基于植被光学特性传感的技术通常受限于在土壤背景上检测杂草（即光秃秃的土壤背景中的绿色检测），并且不适合检测生长中的作物中的杂草。基于视觉的点喷系统可以区分植被种类。建立可靠的、鲁棒的杂草/作物识别模型是基于视觉的现场喷洒技术发展的关键之一。传统上，基于视觉的杂草/作物识别模型的开发高度依赖于对杂草和作物之间定义的颜色、纹理和形态特征的先验知识的图像分析。但是，这可能无法推广到不同的农田和多种杂草。机器学习和计算机视觉的最新技术进步为在非结构化田地条件下开发鲁棒可靠的基于视觉的杂草/作物识别模型提供了新的机会。目前，训练深度机器学习模型需要大量的标记图像。为了弥补人工标记任务并加快开发过程，本项目提出了一种基于传统图像分析和生成对抗网络（gan）相结合的新管道来生成逼真的合成图像。此外，该项目还将重点解决当前基于视觉的斑点喷涂发展在识别模型泛化能力和喷涂效率方面的瓶颈问题。具体来说，这个项目的目标是：(1)建立两个图像数据库（杂草和作物），允许通过开发的生成管道快速生成带注释的合成图像；(2)开发基于大量合成图像的鲁棒轻量级深度学习检测模型；(3)将所开发的杂草检测系统集成到喷雾臂中进行现场喷洒，并验证其在现场条件下高效现场喷洒（行驶速度> 8 km/h）的可行性。示范将在甜菜地里进行。对喷涂精度和效率进行评价。