Fruit Detection and Recognition Based on Deep Learning for Automatic Harvesting: An Overview and Review

• Published in: Agronomy (Basel) Vol. 13; no. 6; p. 1625
• Main Authors: Xiao, Feng, Wang, Haibin, Xu, Yueqin, Zhang, Ruiqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Agriculture; agronomy; Artificial neural networks; automatic harvesting; Automation; Cameras; Classification; computer vision; Crop diseases; Cultivars; current challenge; data collection; Deep learning; Detectors; fruit detection; fruit recognition; Fruits; Harvest; Harvesting; Internet of Things; Machine learning; Neural networks; Plant diseases; Recognition; Researchers; Robotics; Target detection; vision; China
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy13061625

Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in particular have demonstrated the ability to attain accuracy and speed levels comparable to those of humans in some fruit detection and automatic harvesting fields. This paper presents a comprehensive overview and review of fruit detection and recognition based on DL for automatic harvesting from 2018 up to now. We focus on the current challenges affecting fruit detection performance for automatic harvesting: the scarcity of high-quality fruit datasets, fruit detection of small targets, fruit detection in occluded and dense scenarios, fruit detection of multiple scales and multiple species, and lightweight fruit detection models. In response to these challenges, we propose feasible solutions and prospective future development trends. Future research should prioritize addressing these current challenges and improving the accuracy, speed, robustness, and generalization of fruit vision detection systems, while reducing the overall complexity and cost. This paper hopes to provide a reference for follow-up research in the field of fruit detection and recognition based on DL for automatic harvesting.