As the global population continues to increase, so does its dependance on food, which has led to the development of more efficient food production methods. Greenhouse horticulture and genetically modified seeds have increased crop yield and decreased water and energy consumption.
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As the global population continues to increase, so does its dependance on food, which has led to the development of more efficient food production methods. Greenhouse horticulture and genetically modified seeds have increased crop yield and decreased water and energy consumption. However, delicate greenhouse-grown vegetables continue to necessitate manual labour. The purpose of this thesis is to design and evaluate a vision system capable of detecting the spatial structure of greenhouse vegetation in order to automate plant repositioning, pruning, and leaf removal. The system employs a robotic arm and a single camera to generate custom plant features, match them, and generate a 3D model of the plant’s structure. The thesis discusses the system’s requirements, design, and evaluation, including the segmentation of plant outlines using deep learning networks, the construction of a 2D plant structure using constrained Delaunay triangulation, and triangulation-based depth calculation. Analysis of three-dimensional precision reveals a correlation between camera positional errors and the algorithm’s depth estimation. The findings support the viability of using a vision system to automate greenhouse duties if certain modifications are made. Contributing to ongoing efforts to increase greenhouse agriculture’s efficiency and reduce manual labour.
