Semantic segmentation of agricultural images: A survey

Abstract

As an important research topic in recent years, semantic segmentation has been widely applied to image understanding problems in various fields. With 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 methods. Semantic segmentation methods have revolutionized the development of agricultural automation and are commonly used for crop cover and type analysis, pest and disease identification, etc. We first give a review of the recent advances in traditional and deep learning methods for semantic segmentation of agricultural images according to different segmentation principles. Then we introduce the traditional methods that can effectively utilize the original image information and the powerful performance of deep learning-based methods. Finally, we outline their applications in agricultural image segmentation. In our literature, we identify the challenges in agricultural image segmentation and summarize the innovative developments that address these challenges. The robustness of the existing segmentation methods for processing complex images still needs to be improved urgently, and their generalization abilities are also insufficient. In particular, the limited number of labeled samples is a roadblock to new developed deep learning methods for their training and evaluation. To this, segmentation methods that augment the dataset or incorporate multimodal information enable deep learning methods to further improve the segmentation capabilities. This review provides a reference for the application of image semantic segmentation in the field of agricultural informatization.