Digital technology for determining quality indicators and classification of apple fruits based on computer vision and deep learning

  • 1 Kazakh National Agrarian Research University, Almaty, Kazakhstan


This article examines the use of computer vision and deep learning to automatically determine key quality indicators of apples, enhancing product quality. It describes a digital method for measuring apple size, ripeness, and variety classification using an automated optoelectronic system, achieving an accuracy of at least 86%. Advantages, limitations, and potential productivity benefits for Kazakhstan’s apple production are discussed. An algorithm developed with OpenCV in Python analyzes apple images to determine diameter, height, surface area, red color proportion, and external defects. Tested on “Sinap Almaty” apples, the method measures linear dimensions, crosssectional area, and redness percentage.



  1. Bhatt, A.K., Pant, D., 2015. Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation. AI & Soc. 30 (1), 45–56
  2. M.M. Sofu , O. Erb, M.C. Kayacan , B. Cetisi. 2016. Design of an automatic apple sorting system using machine vision. Computers and Electronics in Agriculture 127 (2016) 395–405
  3. Payman Moallem a,b,*, Alireza Serajoddin c, Hossein Pourghassem d,c Computer vision-based apple grading for golden delicious apples based on surface features INFORMATION PROCESSING IN AGRICULTURE 4 (2017) 33–40
  4. Nurtuleuov*, A., Moldazhanov, A., Kulmahambetova, A., & Zinchenko, D. (2021). Obosnovanie metoda i algoritma opredeleniya pokazatelej kachestva yablok i avtomaticheskoj sortirovki ih na kategorii. Izdenister Natigeler, (3 (91), 125–133. 2021/14
  5. Anjaneya, L & U., Dr. (2023). Machine learning approach to the classification and identification of hand gesture recognition using python. International Journal of Recent Scientific Research. 14. 4372-4377. 10.24327/ijrsr.20231411.0821.
  6. Verdhan, Vaibhav. (2021). Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras. 10.1007/978-1-4842-6616-8.

Article full text

Download PDF