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

  • 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

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
How to Cite
MUREȘAN, H.B.; CĂLIN, A.D.; COROIU, A.M.. Overview of Recent Deep Learning Methods Applied in Fruit Counting for Yield Estimation. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 65, n. 2, p. 50-65, dec. 2020. ISSN 2065-9601. Available at: <http://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/58>. Date accessed: 18 oct. 2021. doi: https://doi.org/10.24193/subbi.2020.2.04.
Section
Articles