Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation

In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.

@article{Malik2016,
  title     = {Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {5},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Zeeshan Malik and Sheikh Ziauddin and Ahmad R. Shahid and Asad Safi},
  doi       = {10.14569/IJACSA.2016.070569},
  url       = {https://doi.org/10.14569/IJACSA.2016.070569}
}