Vol. 17 / 2022 – International Conference on Virtual Learning
Automatic detection of ripe fruits with Python – an interactive learning activity with cross-disciplinary approach
Ștefan-Alfred MARIS, Alexandra SUTA, Cristian Mircea MUSCAI, Simina MARIS
Automatic fruit harvesting addresses several issues, which can be considered as an independent computer science project, among them being the correct detection of a ripe fruit. The main focus of this article is to describe a project which derives from the detection of a ripe fruit and which is used within an interdisciplinary learning activity. The learning activity is intended for but not restricted to coding courses (like Coding with Patience) at high school and bachelor degree. We describe the original algorithms used for detecting the fruit, its level of ripeness according to its colour, and its dimensions. As the image is taken in RGB format, the colour recognition algorithm deals with the levels of red, green and blue of the image, but these levels are dependent on an optimal lighting of the scene, which is achieved using a hardware solution. The algorithms are calibrated to identify red and yellow fruits and with proper adjustments they can be extended to other types of ripe fruits (e.g., oranges, lemons, red apples, peaches, etc.). The detection is performed using a camera module attached to a Raspberry Pi 3B+ system and the image analysis is performed with Python.
Python, Computer vision, Interdisciplinary learning, Colour recognition
CITE THIS PAPER AS:
Ștefan-Alfred MARIS, Alexandra SUTA, Cristian Mircea MUSCAI, Simina MARIS, "Automatic detection of ripe fruits with Python – an interactive learning activity with cross-disciplinary approach", International Conference on Virtual Learning, ISSN 2971-9291, ISSN-L 1844-8933, vol. 17, pp. 155-165, 2022. https://doi.org/10.58503/icvl-v17y202213