Beyaz Abdullah, Saripinar Zülfi
Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, Ankara University, Ankara, Türkiye.
Turkish Sugar Factories Corporation, Sugar Institute, Ankara, Türkiye.
Sci Rep. 2025 May 13;15(1):16574. doi: 10.1038/s41598-025-98253-z.
The global seed coating market is expected to experience substantial growth, increasing from a 2023 valuation of USD 2.0 billion to an estimated value of USD 3.1 billion by 2028. This growth surge is primarily due to the consistent introduction of innovative seed coating technologies and formulations, which are designed to enhance seed quality, improve crop performance, and prioritize sustainability in agriculture. For this reason, the goal of this work is to categorize coated sugar beet seeds based on coating defects using the YOLO (You Only Look Once) algorithm. Coating defects can have a substantial impact on seed quality and germination rates; thus, seeds must be carefully identified and classified. Using the YOLO algorithm, it is possible to detect and categorize coating defects on sugar beet seeds, thereby enhancing seed quality and production swiftly, and effectively. To this end, totally high-resolution (3000 × 4000 pixel) RGB images of 2000 coated sugar beet seeds were used, which were obtained from a top-side open shooting box under constant 1150 lx daylight conditions to create an original database. The classification was performed on sugar beet seeds with normal, broken, star-shaped, and adherent coatings, based on 80% training and 20% validation rates with the YOLOv10-N, YOLOv10-L, and YOLOv10-X models. According to evaluations, the best test accuracies were obtained from YOLOv10X, 93% for normal coating, 94% for broken coating, 94% for star-shaped coating, and 95% for adherent coating. Additionally, the best inference times were obtained from YOLOv10N: 11.5 ms for normal coating, 11.7 ms for broken coating, 11.4 ms for star-shaped coating, and 11.9 ms for adherent coating. Therefore, it is possible that the negative effects of changing operating conditions can be brought into full control with image processing technologies.
全球种子包衣市场预计将大幅增长，从2023年的20亿美元估值增长到2028年预计的31亿美元。这种增长激增主要归因于不断推出创新的种子包衣技术和配方，这些技术旨在提高种子质量、改善作物性能并优先考虑农业的可持续性。因此，这项工作的目标是使用YOLO（You Only Look Once）算法根据包衣缺陷对包衣甜菜种子进行分类。包衣缺陷会对种子质量和发芽率产生重大影响；因此，必须仔细识别和分类种子。使用YOLO算法，可以检测和分类甜菜种子上的包衣缺陷，从而迅速有效地提高种子质量和产量。为此，使用了2000颗包衣甜菜种子的全高分辨率（3000×4000像素）RGB图像，这些图像是在1150勒克斯恒定日光条件下从顶部开口的拍摄箱中获得的，以创建一个原始数据库。基于80%的训练率和20%的验证率，使用YOLOv10-N、YOLOv10-L和YOLOv10-X模型对具有正常、破损、星形和附着包衣的甜菜种子进行分类。根据评估，YOLOv10X获得了最佳测试准确率，正常包衣为93%，破损包衣为94%，星形包衣为94%，附着包衣为95%。此外，YOLOv10N获得了最佳推理时间：正常包衣为11.5毫秒，破损包衣为11.7毫秒，星形包衣为11.4毫秒，附着包衣为11.9毫秒。因此，通过图像处理技术有可能完全控制操作条件变化的负面影响。
