Artificial Intelligence in Soybean Seed Classification: Techniques, Applications, and Research Trends

Soybean classification is essential for ensuring quality and standardization, determining commercial value, and optimizing processes throughout the agricultural supply chain. Due to the complexity of the parameters assessed, advanced technologies such as artificial intelligence (AI) and machine learning have emerged as promising tools to improve the accuracy and automation of these analyses. This study conducted a systematic literature review (SLR) following the PRISMA protocol guidelines. The review was conducted using the Scopus database, with no temporal restriction, covering studies indexed up to August 2024. A total of 56 studies were selected and analyzed. The objective was to identify the main technologies and methods used to classify soybeans using AI. The review mapped the most commonly used technologies, including RGB, near‐infrared (NIR), and hyperspectral imaging, and identified a growing dominance of deep learning approaches, particularly convolutional neural networks (CNNs). RGB imaging is the most widely used technique, while hyperspectral imaging supports more detailed internal quality assessments. Despite the high reported accuracies, recurring limitations across the reviewed studies include dataset variability and the predominance of controlled laboratory conditions. These findings provide a structured overview of current research trends, highlight key challenges for real‐world applications, and suggest practical directions for selecting appropriate AI techniques in soybean seed classification.