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Research Article | Open Access |

Smartphone Image based Agricultural Product Quality and Harvest Amount Prediction Method

Author 1: Kohei Arai Author 2: Osamu Shigetomi Author 3: Yuko Miura Author 4: Satoshi Yatsuda
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 10, No. 9 · Published 2019

DOI: https://doi.org/10.14569/IJACSA.2019.0100904

Abstract

A method for agricultural product quality and harvest amount prediction by using smartphone camera image is proposed. It is desired to predict agricultural product quality and harvest amount as soon as possible after the sowing. In order for that, satellite imagery data, UAV camera based images, ground based camera images are used and tried These methods do cost significantly and these do not work so well due to some reasons, in particular, most of farmers cannot use these properly. The proposed method uses just smartphone camera acquired images. Therefore, it is totally easy to use. If the results of prediction of product quality and harvest amount are not satisfied, then farmers have to add some additional fertilizer at the appropriate time. The experimental results with soy plantations show some possibility of the proposed method.

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How to Cite this Article

Arai, K., Shigetomi, O., Miura, Y., & Yatsuda, S. (2019). Smartphone Image based Agricultural Product Quality and Harvest Amount Prediction Method. International Journal of Advanced Computer Science and Applications, 10(9). https://doi.org/10.14569/IJACSA.2019.0100904

Arai, Kohei, et al.. "Smartphone Image based Agricultural Product Quality and Harvest Amount Prediction Method." International Journal of Advanced Computer Science and Applications, vol. 10, no. 9, 2019, https://doi.org/10.14569/IJACSA.2019.0100904.

@article{Arai2019,
  title     = {Smartphone Image based Agricultural Product Quality and Harvest Amount Prediction Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {9},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Osamu Shigetomi and Yuko Miura and Satoshi Yatsuda},
  doi       = {10.14569/IJACSA.2019.0100904},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100904}
}

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