Deep learning in produce perception of harvesting robots: A comprehensive review

Abstract

In recent years, the global demand for produce has surged, alongside labor shortages, driving the development of agricultural automation, particularly in harvesting robots. Deep learning-based computer vision algorithms have become key to produce perception, demonstrating significant potential. We systematically review the current application of deep learning in produce perception for harvesting robots, providing an in-depth analysis of existing public datasets, with a focus on 2D produce recognition and 3D produce localization. Furthermore, we review and analyze the existing algorithms, highlighting their limitations and challenges. In addition, we explore future research directions of deep learning in produce perception, aiming to promote the continued advancement and innovation of technologies in this area. • Comprehensive review of deep learning in produce perception for harvesting robots. • Analysis of the background and status of deep learning-based produce perception. • Overview of public datasets for produce perception models training and evaluation. • Detailed review of 2D produce recognition and 3D produce localization advancements. • Identification of challenges and trends for deep learning in produce perception.