Broccoli Grading Based on Improved Convolutional Neural Network Using Ensemble Deep Learning

The demand for broccoli in Indonesia has been increasing significantly, with an annual growth of approximately 15% to 20%. However, the supply availability remains insufficient, and its quality is often inconsistent. Therefore, a grading process is needed to classify broccoli into grades A, B, and C based on color, size, and shape. Currently, the grading process is carried out solely by market intermediaries, while farmers and the general public have a limited understanding of this process. This research developed an automated grading method using a Convolutional Neural Network (CNN) based on two broccoli images’ top and side views. Three main parameters, namely color, size, and shape, were identified from these images and used as grading determinants. An ensemble deep learning technique was applied by training each parameter separately using several CNN models, namely ResNet50, EfficientNetB2, VGG16, and Improved CNN. These were then combined in the testing phase using a voting technique. The test was conducted 64 times with various model combinations to achieve the best accuracy. A significant contribution of the Improved CNN lies in the shape feature, which achieved a maximum performance of 95%. This study also compared evaluation metrics such as precision, recall, F-Score, and accuracy across different model combinations.

@article{Imaduddin2025,
  title     = {Broccoli Grading Based on Improved Convolutional Neural Network Using Ensemble Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Zaki Imaduddin and Yohanes Aris Purwanto and Sony Hartono Wijaya and Shelvie Nidya Neyman},
  doi       = {10.14569/IJACSA.2025.0160265},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160265}
}