Identification and classification of fruits through robotic system by using artificial intelligence
Main Article Content
Abstract
The purpose of this research paper is to facilitate the work of industries that deal with the classification of different types of products, specifically in our case from the fruit and vegetable industry using Artificial Intelligence. The most important part of this work was the processing of images for fruit identification. For the classification to be as accurate as possible, it was necessary to use more samples of different types of fruits. To achieve the desired results, a total of 350 samples were needed, where to reach the number of these samples we photographed several types of fruits at different angles. Using the Python programming language, which has many libraries that are open sources and can be advanced by various professionals in the field of programming, facilitates image processing. For this purpose, the OpenCV library was used to generate photos so that fruits can be identified more easily. TensorFlow is used as a platform to teach machines to adapt our samples to the language that the robotic system understands.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
https://zentronlabs.com/systems/optical-fruit-grading-and-sorting-machine
Luger, G. F. (2005). Artificial intelligence: structures and strategies for complex problem solving. Pearson education.
Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc..
Nilsson, N. J. (2010). The quest for artificial intelligence: A history of ideas and achievements. Cambridge University Press.
Bishop, C. M. (2016). Pattern recognition and machine learning. Springer New York.
Wang, Y., Zhu, S., Zhang, Q., Zhou, R., Dou, R., Sun, H., ... & Zhang, Y. (2021). A visual grasping strategy for improving assembly efficiency based on deep reinforcement learning. Journal of Sensors, 2021, 1-11. https://doi.org/10.1155/2021/8741454.
Ying, K. C., Pourhejazy, P., Cheng, C. Y., & Cai, Z. Y. (2021). Deep learning-based optimization for motion planning of dual-arm assembly robots. Computers & Industrial Engineering, 160, 107603. https://doi.org/10.1016/j.cie.2021.107603
Shahria, M. T., Sunny, M. S. H., Zarif, M. I. I., Ghommam, J., Ahamed, S. I., & Rahman, M. H. (2022). A Comprehensive Review of Vision-Based Robotic Applications: Current State, Components, Approaches, Barriers, and Potential Solutions. Robotics, 11(6), 139. https://doi.org/10.3390/robotics11060139
Pajaziti, A., Buza, S., Gojani, I., Safaric, R., & Kopacek, P. (2009). Cost Oriented Robots for Kosovo. INHALT Seite, 58.
Dobot Magician Manuals. https://www.dobot.cc/dobot-magician/product-overview.html
Intelligent Robotic Arms Provider. DOBOT. https://www.dobot.cc/dobot-magician/product-overview.html
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Zheng, X. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
TensorFlow (2021). Object Detection Camera Demo. https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/auto_examples/object_detection_camera.html#sphx-glr-auto-examples-object-detection-camera-py