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

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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.