In viticulture, it is necessary to accurately analyze the condition of plants and regularly monitor their growth. Traditional methods based on manual inspections of each plant are time-consuming and often inefficient. Therefore, it is imperative to develop an automated system that enables fast, accurate and objective analysis of large areas.
The goal of the thesis is to develop an automated vineyard detection system that will provide reliable and accurate results. This will enable efficient analysis of large vineyard areas without the need for manual inspections of each individual plant. We approach the vineyard detection problems through a segmentation task for which we use computer vision and deep learning methods, with the main tool being the SAM (Segment Anything Model) model, particularly, its latest version SAM 2. The key part of the methodology includes pre-labeling of images, model training and optimization of results. We use libraries such as PyTorch, NumPy and OpenCV, which enable flexible data processing and visualization of results. To validate the model, we use metrics such as IoU (Intersection over Union) and F1-score, which evaluate the success of segmentation based on manually labeled images. The final results are compared with the base SAM 2 model.
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