A Low-Cost YOLOv5-Based System for Automated Classification of Maize Seed Translucency

Abstract

The physical quality of seeds is a critical determinant of sorting efficiency and crop productivity, yet conventional assessment approaches are often labor-intensive, invasive, and time-consuming.To address these limitations, computer vision-based methods have been increasingly adopted; however, most existing techniques rely primarily on reflected visible light, thereby capturing only surface-level features and limiting the detection of internal defects.In this study, a low-cost imaging system integrating both reflection and transmission of visible light was developed to enhance the characterization of maize seed translucency.By enabling simultaneous acquisition of information from the two principal faces of white maize seeds, a more comprehensive representation of both external morphology and internal structural variations was achieved.A comparative analysis was conducted between the conventional reflection-based method and the proposed imaging approach, with correlation coefficients between seed faces determined as 0.62 and 0.84, respectively, indicating a substantial improvement in feature consistency and information richness.A dedicated dataset was subsequently constructed using both imaging techniques and employed to train a YOLOv5s-based detection model over 200 epochs.The classification performance demonstrated a marked enhancement, with the proposed method achieving an accuracy of 93.07%, compared to 81.5% obtained using the conventional approach.Furthermore, real-time detection capability was validated through the implementation of the optimized imaging system, in which improved inference stability and robustness were achieved under practical operating conditions.The results indicate that the integration of transmission with reflection imaging provides a cost-effective and reliable solution for non-destructive seed quality assessment, offering significant potential for scalable deployment in agricultural sorting systems.