Smart Fruit Identification and Counting using Machine Vision Approach

Estimating fruit yield holds significant importance for farmers as it enables them to make precise resource management decisions for fruit harvesting. The adoption of automated image processing technology not only reduces the human labor required but also enhances the accuracy of ripe fruit estimates. This research delves into the performance of an image processing algorithm designed to count and identify oranges. The study employed a multi-phase approach, starting with the creation of a mask to isolate orange content, followed by the detection of circular shapes within the mask. Lastly, the algorithm filters and counts the identified circles. The outcome of this study revealed that the algorithm demonstrated an impressive success rate of approximately 72.4% in correctly identifying oranges with standard deviation of +/- 12.20.

@article{Shankarpure2023,
  title     = {Smart Fruit Identification and Counting using Machine Vision Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Madhura R. Shankarpure and Dipti D. Patil},
  doi       = {10.14569/IJACSA.2023.0141229},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141229}
}