Abstract Crop grading represents a critical component in enhancing agricultural production efficiency and optimizing supply chain management. Through systematic assessment of crop appearance, quality attributes, and dimensional characteristics, accurate determination of market compliance and evaluation of quality grades and commercial value can be achieved. This systematic review comprehensively analyzes 53 studies published between 2020 and 2025 that employed deep learning-based target detection methods for automated crop grading applications. We establish a taxonomic framework categorizing detection methodologies into three distinct paradigms: two-stage detectors (characterized by high precision but substantial computational overhead), single-stage detectors (offering optimal speed-accuracy trade-offs), and emerging Transformer-based approaches (demonstrating superior global modeling capabilities). Our analysis reveals significant algorithmic evolution trends, with the YOLO series exhibiting dominant application across more than 30 crop varieties, achieving high detection accuracy while maintaining real-time performance capabilities. We systematically identify key technical challenges: sparse feature extraction under complex agricultural environments, severe class imbalance in defect detection tasks, and deployment constraints in edge computing scenarios. We propose a comprehensive solution framework that integrates multimodal data fusion (RGB-D-NIR), lightweight architectural optimization strategies, and semi-supervised learning approaches capable of reducing annotation requirements by 60–80%. Performance benchmarking on public datasets demonstrates that target detection methods exhibit superior engineering applicability compared to segmentation approaches, with enhanced inference speed and improved robustness to environmental variations. For future research directions, we categorize priorities into four domains: intelligent annotation systems, environmental adaptability enhancement, model compression techniques, and cross-domain transfer learning. This review provides a comprehensive technical framework for deep learning-driven crop grading, offering actionable insights for researchers and practical deployment guidelines for precision agriculture applications. We anticipate this review will inspire broader adoption of deep learning target detection methodologies in agricultural domains, fostering technological advancement and innovation in this field.
Zhu et al. (Wed,) studied this question.