Semantic segmentation of agricultural images:A survey

作     者：Zifei Luo Wenzhu Yang Yunfeng Yuan Ruru Gou Xiaonan Li 

作者机构：School of Cyber Security and ComputerHebei UniversityHebei 071002China Hebei Machine Vision Engineering Research CenterBaodingHebei 071002China College of EducationHebei UniversityHebei 071002China 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2024年第11卷第2期

页      面：172-186页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：the Post-graduate's Innovation Fund Project of Hebei University(HBU2022ss037) the High-Performance Computing Center of Hebei University 

主　　题：Semantic segmentation Agricultural images Deep learning Convolution neural networks 

摘      要：As an important research topic in recent years,semantic segmentation has been widely applied to image understanding problems in various *** the successful application of deep learning methods in machine vision,the superior performance has been transferred to agricultural image processing by combining them with traditional *** segmentation methods have revolutionized the development of agricultural automation and are commonly used for crop cover and type analysis,pest and disease identification,*** frst give a review of the recent advances in traditional and deep learning methods for semantic segmentation of agricultural images according to different segmentation *** we introduce the traditional methods that can effectively utilize the original image information and the powerful performance of deep learningbased ***,we outline their applications in agricultural image *** our literature,we identify the challenges in agricultural image segmentation and summarize the innovative developments that address these *** robustness of the existing segmentation methods for processing complex images still needs to be improved urgently,and their generalization abilities are also *** particular,the limited number of labeled samples is a roadblock to new developed deep learning methods for their training and *** this,segmentation methods that augment the dataset or incorporate multimodal information enable deep learning methods to further improve the segmentation *** review provides a reference for the application of image semantic segmentation in the field of agricultural informatization.