Automated Fruit Grading in Precise Agriculture using You Only Look Once Algorithm

In the realm of precision agriculture, the automated grading of fruits stands as a critical endeavor, serving to maintain consistent quality assessment and streamline the sorting process. Traditional methods based on computer vision and deep learning techniques have both been explored extensively in the context of fruit grading, with the latter gaining prominence due to its superior performance. However, the existing research landscape in the domain of deep learning-based fruit grading confronts a compelling challenge: striking a balance between accuracy and computational cost. This challenge has been consistently noted through an extensive analysis of prior studies. In response, this study introduces an innovative approach built upon the YOLOv5 algorithm. This methodology encompasses the creation of a bespoke dataset and the division of data into training, validation, and testing sets, facilitating the training of a robust and computationally efficient model. The findings of the experiments and the subsequent performance evaluation underscore the effectiveness of the proposed method. This approach yields significant improvements in both accuracy and computational efficiency, thus addressing the ongoing challenge in deep learning-based fruit grading. Therefore, this study contributes valuable insights into the field of automated fruit grading, offering a promising solution to the trade-off between accuracy and computational cost while demonstrating the practical viability of the YOLOv5-based approach.

@article{Zhang2023,
  title     = {Automated Fruit Grading in Precise Agriculture using You Only Look Once Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Weiwei Zhang},
  doi       = {10.14569/IJACSA.2023.01410119},
  url       = {https://doi.org/10.14569/IJACSA.2023.01410119}
}