Segmentação de regiões de interesse em imagens de plantas da subespécie Acer palmatum - Braun, Vinícius Caron (2023)
Image segmentation in plants serves for the identification of the specific plant, differentiating it from the rest of the image composition. The problem of plant segmentation is often contained in the difficulty of accurately tracing the segments around the plant's leaf, which is often serrated and has small detailed elements. Color, hue, and lighting are also factors that interfere with the segmentation process and image treatment. After all, the steps of image treatment are based on pixelation, which is composed of factors such as contrast and color. This is further complicated when dealing with an image with a colorful and cluttered background. Therefore, the present work aims to optimize the image segmentation of the Acer palmatum subspecie and its varieties, chosen for having foliage characteristics that are slightly more challenging than usual. This optimization will facilitate the classification process among its different varieties, considering that the plant has serrated leaves that change color according to the season. To achieve this goal, this work makes use of technologies mainly related to the Python programming language, which has various libraries such as Open Source Computer Vision Library (OpenCV) and Remove Background (RemBg), with resources focused on Computer Vision and Machine Learning, the two main topics of this work. Different experiments were conducted in this work using images of the plant captured against a white background and a cluttered background. The results obtained indicate that segmentation using basic methods from the OpenCV library is not very efficient in images with a cluttered background, showing poor visualization and identification of the plant image after processing. However, in terms of direct segmentation of Acer palmatum plant leaves, the RemBg library, with the U2-Net Deep Learning algorithm, showed better segmentation efficacy in images with a cluttered background, highlighting the plant leaf from the rest of the image. In images with a white background, the algorithm segmented much more easily, as expected. The Mask R-CNN (Mask Region-based Convolutional Neural Network) Deep Learning algorithm was also employed, but it showed low efficacy due to the limited number of training samples used.