综述果树图像分割研究，指出缺乏通用数据集与模型。
Review of Fruit Tree Image Segmentation
- 按方法、图像、任务、果实分类系统梳理158篇论文
- 发现现有研究普遍缺少跨任务跨环境的通用分割方案
- 提出6项未来方向，助力构建通用果树分割模块
果树图像分割是自动化农业任务（如表型分析、采摘、喷洒和修剪）的关键问题。本文基于新设计的爬虫方法收集了158篇相关论文，聚焦于果树正面视图，通过方法、图像、任务和果实的层级分类体系进行系统性回顾。该分类框架帮助读者直观把握研究全貌。分析显示，先前研究最显著的不足在于缺乏可广泛适用的通用数据集与分割模型。为此，本文提出了六项重要未来研究方向，旨在推动构建具备通用性的果树分割模块。
原文摘要 · Abstract (English)
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 to specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees and based on 158 relevant papers collected using a newly designed crawling review method. These papers are systematically reviewed based on a 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 tasks are suggested, with the expectation that these will pave the way to building a versatile tree segmentation module.
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