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Using the YOLOv3 algorithm with preand post-processing procedures for fruit detection by an apple-picking robot

The development of robotic harvesting can help reduce the share of heavy manual labor in horticulture that reaches now 40%, as well as crop shortages, which reach up to 50%. Fruit picking robots have been developing since the late 1960s. However, no existing prototype is used in practice due to the low speed of harvesting and the large proportion of unrecognized fruits remaining on trees. The paper aims to develop an algorithm for detecting apples in images that can work quickly and find as many apples as possible. For this purpose, the use of the YOLOv3 convolutional neural network has been proposed, accompanied by special pre- and post-processing procedures. The procedures aim to improve the quality of apple recognition, including situations of the presence of shadows, glare, various damages to apples, empty gaps between the leaves, which could be mistaken for apples, overlapping apples by branches, leaves, and other apples. The algorithm recognizes both red and green apples. It can work with images of single apples in close-up photographs as well as with images of many apples in general pictures. The algorithm quality was evaluated on a test set of 818 images of red and green apples (5142 apples in total). The average apple detection time was 19 ms, the percentage of objects mistaken for apples turned out to be at the level of 7.8%, and the share of undetected apples at 9.2%. Both the average detection time and the error rates turned out to be noticeably shorter than in all known similar systems. Read more...