A brief description of the basic stages of image processing is given to pay attention to the segmentation stage as a
possible way to improve efficiency in decision-making. The main characteristics of the presented model are visual signs, such as
color, shape, the presence of a stem, and others. Due to the different approaches in image processing, a high level of truthfulness is
achieved, which is expressed in the percentage ratio of the accuracy of decision-making and varies in the range from 90 to 96%.
Therefore, the results obtained in this work make it possible to automate the process of visual inspection with the prospect of increasing
the speed and quality of product sales for the consumer.
 J. García-Alcaraz, A. Maldonado-Macías, G. Cortes-Robles. Lean Manufacturing in the Developing World,117(8), 827–891. 2014, Available: https://link.
 W. Sullivan, T. McDonald, & Van Aken, E. (2002).Equipment replacement decisions and lean manufacturing.
18(3–4), 255–265. Available: https://www.sciencedirect.com/science/article/abs/pii/S0736584502000169
 G. Kreiman. Biological and Computer Vision 52-53 . Available: https://www.cambridge.org/ core/books/biological-and-computervision/BB7E68A69AFE7A322F68F3C4A297F3CF
 R. Szeliski. Computer Vision: Algorithms and Applications.42-43. 2022. Available: https://szeliski.org/Book/ J. Watt, R. Borhani, A. Katsaggelos. Machine Learning
Refined: Foundations, Algorithms, and Applications 45-46 . Available: https://people.engr.tamu.edu/guni/csce421/files/Machine_Learning_Refined...
 A. Koul, S. Ganju, M. Kasam. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensor-
Flow. 25-26. 2019. Available: https://www.oreilly.com/library/view/practical-deep-learning/9781492034858/
 J. Kelleher. Deep Learning 12-13 . Available:https://direct.mit.edu/books/book/4556/Deep-Learning
 T. Amaratunga. Deep Learning on Windows: Building Deep Learning Computer Vision Systems on Microsoft Windows 88-89 . Available: https://link.springer.
 M. Favorskaya, L. Jain. Computer Vision in Control Systems. 55-56. 2015. Available: https://link.springer.
 K. Mehrotra, Ch. Mohan, H. Huang. Anomaly Detection Principles and Algorithms. 165-166. 2017. Available:
https://www.amazon.com/Detection-Principles-Algorithms-Terrorism-Computa... S. Prince. Computer Vision: Models, Learning, and
Inference. 222-223. 2022. Available: http://www. computervisionmodels.com
 G. Garrido, P. Joshi. OpenCV 3.x with Python By Example,33-34 . Available: https://www.packtpub.com/product/opencv-3x-with-python-byexample-second-...
 N. Madanchi. Model-Based Approach for Energy and Resource Efficient Machining Systems. 111-112. 2022. Available: https://link.springer.com/book/10.1007/978-3-030-87540-4
 M. Rafiei. Self-Supervised Learning A-Z: Theory & Hands-On Python. 24-25. 2022. Available: https://www.udemy.com/course/self-supervised-learning/
 N. Sebe, M. Lew. Robust Computer Vision. 54-55. 2022.Available: https://link.springer.com/book/10.1007/978-94-017-0295-9
 J. Marks. Tunnel vision in computer vision: can ChatGPT see?. 2022. Internet Journal. Available:https://medium.com/voxel51/tunnel-vision-in-computervision-can-chatgpt-s...
 A. Rosebrock. Deep Learning for Computer Vision with Python. 55-56. 2017. Available: https://bayanbox.ir/view/5130918188419813120/Adrian-Rosebrock-Deep-Learn...
 G. Blokdyk. Object detection Complete Self-Assessment Guide. 42-43. 2021. Available: https://www.amazon.com/dp/0655324380?tag=uuid10-20
 R. Shanmugamani. Deep Learning for Computer Vision.68-69. 2021. Available: https://www.oreilly.com/ library/view/deep-learning-for/9781788295628/edf4fbcccca0-4aaa-b9f4-d7c2292c520d.x.html
 J. Howse, P. Joshi, M. Beyeler. OpenCV: Computer Vision Projects with Python. 122-123. 2020. Available:https://www.oreilly.com/library/view/opencv-computervision/