Overview of Recent Deep Learning Methods Applied in Fruit Counting for Yield Estimation

Authors

  • H.B. Mureșan Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, 1 Kogalniceanu St., 400084 Cluj-Napoca, Romania
  • A.D. Călin Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, 1 Kogalniceanu St., 400084 Cluj-Napoca, Romania
  • A.M. Coroiu Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, 1 Kogalniceanu St., 400084 Cluj-Napoca, Romania

DOI:

https://doi.org/10.24193/subbi.2020.2.04

Abstract

This paper is an overview of the latest advancements of image recognition for fruit counting and yield estimation. Considering this domain is developing rapidly, we have considered the cutting-edge literature in the field, for the last 5 years, focused on the task of yield estimation by detecting and counting fruit in the tree canopy. This is a much more complex task than the classification of fruit post-harvesting, which has been more widely reviewed. Moreover, we identify the major challenges and propose the next steps for advancing this research field.

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Published

2020-12-09

Issue

Vol. 65 No. 2 (2020)

Section

Articles

License

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