General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
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Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2024 Vol.16(1): 29-34
DOI: 10.7763/IJCTE.2024.V16.1351

Deep Learning-Based Approach for Tomato Classification in Complex Scenes

Mikaël A. Mousse1,*, Béthel C. A. R. K. Atohoun2, and Cina Motamed3
1. Institut Universitaire de Technologie, Université de Parakou, Parakou, Bénin
2. Département Informatique, Ecole Supérieure de Gestion d’Informatique et des Sciences, Cotonou, Bénin
3. Institut Universitaire de Technologie de l’Indre, Université d’Orléans, Orléans, France
Email: (M.A.M.); (B.C.A.R.K.A); (C.M.)
*Corresponding author

Manuscript received August 30, 2023; revised September 22, 2023; accepted January 23, 2024; published February 25, 2024

Abstract—Tracking ripening tomatoes is time consuming and labor intensive. Artificial intelligence technologies combined with those of computer vision can help users optimize the process of monitoring the ripening status of plants. To this end, we have proposed a tomato ripening monitoring approach based on deep learning in complex scenes. The objective is to detect mature tomatoes and harvest them in a timely manner. The proposed approach is declined in two parts. Firstly, the images of the scene are transmitted to the pre-processing layer. This process allows the detection of areas of interest (area of the image containing tomatoes). Then, these images are used as input to the maturity detection layer. This layer, based on a deep neural network learning algorithm, classifies the tomato thumbnails provided to it in one of the following five categories: green, brittle, pink, pale red, mature red. The experiments are based on images collected from the internet gathered through searches using tomato state across diverse languages including English, German, French, and Spanish. The experimental results of the maturity detection layer on a dataset composed of images of tomatoes taken under the extreme conditions, gave a good classification rate.

Keywords—tomato detection, tomato state classification, image processing, deep learning, superpixel segmentation


Cite: Mikaël A. Mousse, Béthel C. A. R. K. Atohoun, and Cina Motamed, "Deep Learning-Based Approach for Tomato Classification in Complex Scenes," International Journal of Computer Theory and Engineering vol. 16, no. 1, pp. 29-34, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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