基于深度学习的类球状水果采摘识别算法研究进展  

Research progress in globular fruit picking recognition algorithm based on deep learning

作　　者：李辉 张俊[2] 俞烁辰 李志鑫 LI Hui;ZHANG Jun;YU Shuochen;LI Zhixin(School of Information Engineering,Huzhou University,Huzhou 313000,Zhejiang,China;Food Science Research Institute,Zhejiang Academy of Agricultural Sciences,Hangzhou 310021,Zhejiang,China)

机构地区：[1]湖州师范学院信息工程学院,浙江湖州313000 [2]浙江省农业科学院食品科学研究所,杭州310021

出　　处：《果树学报》2025年第2期412-426,共15页Journal of Fruit Science

基　　金：国家柑橘产业技术体系(CARS-26-29)。

摘　　要：中国在水果产量方面处于全球领先地位,但因人力资源减少和老龄化问题,传统的人工采摘方式已经无法满足快速高效的采摘需求,研发集成计算机视觉的自动化水果采摘设备成为解决劳动力短缺难题的关键。水果大多呈类球状,相关的识别算法研究居多,探讨了柑橘、蜜桃等类球状水果的识别算法。根据应用场景的不同,分析了传统类球状水果识别算法与基于深度学习的类球状水果识别算法在网络结构方面的差异与改进,对水果采摘识别算法进行总结并提出算法的未来发展趋势。传统算法在简单场景下表现有效,但在复杂环境中往往会受到设计特征的限制,基于深度学习的算法因其高效性和准确性更适合自动化水果采摘的需求。总结了类球状水果识别算法的研究进展,在处理复杂环境时深度学习算法具有良好的有效性和适应性,更适合部署在自动化采摘设备;也提出了未来的研究方向,即通过优化算法性能、数据集构建及扩增,以及结合多模态数据提升算法的精度和适应性。China is a global leader in fruit production,and fruit picking mainly relies on manual labor,which helps to select fruits according to fruit size and quality to reduce loss in this way.Different techniques and tools can be adopted according to the characteristics and picking needs of each fruit crop.However,the present picking field is faced with the problem of decreasing human resources and aging problem.Meanwhile,the traditional manual picking method has become unable to meet the demand for fast and efficient picking.To solve the problem of labor shortage,the research and development of automated fruit picking equipment with integrated computer vision have become the key to solve the problem of labor shortage.It can effectively improve the efficiency and quality of fruit picking.Automatic picking equipment combined with computer vision often uses object detection algorithms to identify objects,and object detection algorithms can be divided into both traditional algorithms and deep learningbased object detection algorithms.Traditional algorithms identify the position and bounding box of a specific object in an image or a video,usually by preprocessing the image(Scaling,grayscale or normalization),feature extraction(using traditional hand-designed features or automatic learning based on machine learning),classification or regression(confirming object class and location),and non-maximum suppression to further optimize and filter detected objects.When traditional fruit detection algorithms process images in complex environments,their limited expression ability and robustness are easily affected by illumination,occlusion and other factors,resulting in a decline in recognition accuracy.Furthermore,with the increase of feature complexity and computation amount,the algorithm processing speed will be reduced.When changing scenes,adding fruit types and updating features,the feature extractor needs to be redesigned and adjusted,and in special cases,the entire system needs to be retrained.Compared with traditional fruit de

关 键 词：水果采摘 目标检测算法 深度学习 卷积神经网络 计算机视觉 

分 类 号：S66[农业科学—果树学]