Azadpour Aram, Mollazade Kaveh, Ramezani Mohsen, Samimi-Akhijahani Hadi
Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.
Department of Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
Sci Rep. 2025 Feb 12;15(1):5206. doi: 10.1038/s41598-025-89358-6.
The agriculture sector is crucial to many economies, particularly in developing regions, with post-harvest technology emerging as a key growth area. The oleaster, valued for its nutritional and medicinal properties, has traditionally been graded manually based on color and appearance. As global demand rises, there is a growing need for efficient automated grading methods. Therefore, this study aimed to develop a real-time machine vision system for classifying oleaster fruit at various grading velocities. Initially, in the offline phase, a dataset containing video frames of four different quality classes of oleaster, categorized based on the Iranian national standard, was acquired at different linear conveyor belt velocities (ranging from 4.82 to 21.51 cm/s). The Mask R-CNN algorithm was used to segment the extracted frames to obtain the position and boundary of the samples. Experimental results indicated that, with a 100% detection rate and an average instance segmentation accuracy error ranging from 4.17 to 5.79%, the Mask R-CNN algorithm is capable of accurately segmenting all classes of oleaster at all the examined grading velocity levels. The results of the fivefold cross validation indicated that the general YOLOv8x and YOLOv8n models, created using the dataset obtained from all conveyor belt velocity levels, have a similarly reliable classification performance. Therefore, given its simpler architecture and lower processing time requirements, the YOLOv8n model was used to evaluate the grading system in real-time mode. The overall classification accuracy of this model was 92%, with a sensitivity range of 87.10-94.89% for distinguishing different classes of oleaster at a grading velocity of 21.51 cm/s. The results of this study demonstrate the effectiveness of deep learning-based models in developing grading machines for the oleaster fruit.
农业部门对许多经济体至关重要，特别是在发展中地区，收获后技术已成为一个关键的增长领域。沙棘因其营养和药用特性而受到重视，传统上是根据颜色和外观进行人工分级。随着全球需求的增加，对高效自动分级方法的需求也在不断增长。因此，本研究旨在开发一种实时机器视觉系统，用于在不同分级速度下对沙棘果实进行分类。最初，在离线阶段，以不同的线性输送带速度（范围为4.82至21.51厘米/秒）获取了一个数据集，该数据集包含根据伊朗国家标准分类的四个不同质量等级的沙棘视频帧。使用Mask R-CNN算法对提取的帧进行分割，以获得样本的位置和边界。实验结果表明，Mask R-CNN算法在所有检查的分级速度水平下，检测率为100%，平均实例分割准确率误差范围为4.17%至5.79%，能够准确分割所有等级的沙棘。五重交叉验证的结果表明，使用从所有输送带速度水平获得的数据集创建的通用YOLOv8x和YOLOv8n模型具有相似的可靠分类性能。因此，鉴于其更简单的架构和更低的处理时间要求，YOLOv8n模型被用于实时模式下评估分级系统。该模型的总体分类准确率为92%，在分级速度为21.51厘米/秒时，区分不同等级沙棘的灵敏度范围为87.10%至94.89%。本研究结果证明了基于深度学习的模型在开发沙棘果实分级机器方面的有效性。
