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    STUDIA INFORMATICA - Ediţia nr.2 din 2020  
         
  Articol:   OVERVIEW OF RECENT DEEP LEARNING METHODS APPLIED IN FRUIT COUNTING FOR YIELD ESTIMATION.

Autori:  H. B. MUREȘAN, A. D. CĂLIN, A. M. COROIU. 		 
       
         
  Rezumat:  
DOI: 10.24193/subbi.2020.2.04

Published Online: 2020-12-09
Published Print: 2020-12-30
pp. 50-65

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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.


Received by the editors: 10 November 2020.
2010 Mathematics Subject Classiffication. 68T45.
1998 CR Categories and Descriptors. I.4.8 [Image Processing and Computer Vision]: Scene Analysis - Object recognition; I.2.6 [Artificial Intelligence]: Learning - Connectionism and neural nets; I.2.10 [Artificial Intelligence]: Vision and Scene Understanding - Intensity, color, photometry, and thresholding.
Key words and phrases. smart-agriculture, deep learning, yield estimation, transfer learning, intersection over union, F1-score. 		 
         
     
         
         
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