| Issue |
E3S Web Conf.
Volume 532, 2024
Second International Conference of Applied Industrial Engineering: Intelligent Production Automation and its Sustainable Development (CIIA 2024)
|
|
|---|---|---|
| Article Number | 02008 | |
| Number of page(s) | 14 | |
| Section | Applied Technological Innovations for Sustainable Industrial Environments | |
| DOI | https://doi.org/10.1051/e3sconf/202453202008 | |
| Published online | 06 June 2024 | |
Fruit Grading based on Deep Learning and Active Vision System
1 ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil - Ecuador
2 Software Engineering Department, University of Granada, 18014, Granada, Spain
3 Computer Vision Center, 08193 -Bellaterra, Barcelona, Spain
* Corresponding author: hvelesac@espol.edu.ec
This paper presents a low-cost computer vision-based solution to obtain the size of fruits without contact. It consists of a low-cost webcam and a cross-shaped laser beam rigidly assembled. The proposed approach acquires and processes the images in real-time. Due to the low computational cost of the proposed algorithm, a robust solution is obtained using a frame redundancy approach, which consists in processing several frames of the same scene and hence computing a robust estimation of the fruit size. The proposed solution is evaluated with different tropical fruits (e.g., banana, avocado, dragon fruit, mamey, papaya, and taxo). Obtained results show on mean average percentage error (MAPE) below 1.50% in the computed sizes.
© The Authors, published by EDP Sciences, 2024
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