A Crawling Review of Fruit Tree Image Segmentation

Il-Seok Oh and Jin-Seon Lee ()
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Il-Seok Oh: Department of Computer Science and Artificial Intelligence/CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea
Jin-Seon Lee: Department of Information Security, Woosuk University, Wanju 55338, Republic of Korea

Agriculture, 2025, vol. 15, issue 21, 1-49

Abstract: Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable for specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees, and 207 relevant papers proposing tree image segmentation in an orchard environment are collected using a newly designed crawling review method. These papers are systematically reviewed based on a four-tier taxonomy that sequentially considers the method, image, task, and fruit. This taxonomy will assist readers to intuitively grasp the big picture of these research activities. Our review reveals that the most noticeable deficiency of the previous studies was the lack of a versatile dataset and segmentation model that could be applied to a variety of tasks and environments. Six important future research topics, such as building large-scale datasets and constructing foundation models, are suggested, with the expectation that these will pave the way to building a versatile tree segmentation module.

Keywords: agricultural task; precision farming; rule-based method; deep learning; computer vision; crawling review (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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