Recognition of West African Indigenous Fruits using a Convolutional Neural Network Model


  • Amarachi M. Udefi Obafemi Awolowo University, Ile-Ife, osun state, 220282, Nigeria,
  • Segun Aina
  • Samuel D. Okegbile
  • Aderonke R. Lawal
  • Adeniran I. Oluwaranti


Convolution neural network (CNN), Fruit_ Recognition, ResNet; VGG 16


The. Fruit recognition involves the extraction and processing of relevant features from fruit images in order to deduce the categories of that fruit. Due to its importance to human health and sustainability, various systems exist for recognition of fruits, although none exist for recognition of west Africa's indigenous fruits. This research developed a fruit recognition system using a convolutional neural network (CNN) based model. Five west Africa indigenous fruits were selected, while “images were directly used as input to CNN based model of (3 convolutional layers, 3 max pooling layers and 1 fully connected layer) for training and recognition without features extraction process. The study further presents a transfer learning on visual geometry group 16 and ResNet models for result comparison. Using the optimal training set, the proposed CNN based model produced a recognition rate of 96%.


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How to Cite

M. Udefi, A. ., Aina, S. ., D. Okegbile, S. ., R. Lawal, A. ., & I. Oluwaranti, A. . (2021). Recognition of West African Indigenous Fruits using a Convolutional Neural Network Model. International Journal of Computer (IJC), 41(1), 10–24. Retrieved from