Classification of Mango Varieties Using a Convolutional Neural Network (CNN) Based on Digital Images
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Abstract
The agricultural and trade sectors in Indonesia stand to benefit significantly from the classification of mango varieties. This study employs a Convolutional Neural Network (CNN) to classify nine types of mangoes using digital images. The dataset was collected directly via smartphone cameras, providing the initial imagery for each mango category. Prior to processing, the images underwent resizing and normalization; the CNN model was then trained to extract features and perform classification through the use of convolutional and dense layers. Evaluation results indicate that the model achieves an accuracy of approximately 90% in identifying mango varieties. In the future, this system is expected to contribute to the advancement of smart agriculture and facilitate the automated classification of fruit.
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References
A. K. Ratha, N. K. Barpanda, P. K. Sethy, dan S. K. Behera, “Automated Classification of Indian Mango Varieties Using Machine Learning and MobileNet‑v2 Deep Features,” Traitement du Signal, vol. 41, no. 2, pp. 345–353, 2024.
C. Unigarro, J. Hernandez, dan H. Florez, “Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops,” Informatics, vol. 12, no. 2, pp. 46, 2025.
L. Chuquimarca, B. Vintimilla, dan S. Velastin, “Classifying Healthy and Defective Fruits with a Multi‑Input Architecture and CNN Models,” arXiv preprint, arXiv:2410.11108, 2024.
S.-L. Wu, H.-Y. Tung, dan Y.-L. Hsu, “Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights,” arXiv preprint, arXiv:2011.11378, 2020.
Z. G. Mursalin, A. Taqwa, dan I. Salamah, “Design and Development of Mango Ripeness Classification Tool using CNN Android-based Platform,” SISTEMASI, vol. 12, no. 1, 2023.
A. K. Pathak, P. Saikia, dan S. Dutta, “Development of a Robust CNN Model for Mango Leaf Disease Detection and Classification: A Precision Agriculture Approach,” ACS Agricultural Science & Technology, vol. 4, no. 8, pp. 806–817, 2024.
Hidra Amnur, Vadreas, A. K., & Ridwan, M. (2024). Aplikasi Pendeteksi Kematangan Tanaman Menggunakan Metode Transformasi Ruang Warna HSI (Hue, Saturation, Intensity) dan K-NN (K- Nearest Neighbor). JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 5(4), 161 -167. https://doi.org/10.62527/jitsi.5.4.318
M. Rahat, et al., “Deep CNN-Based Mango Insect Classification,” in Computer Vision and Machine Learning in Agriculture, Springer, 2021.
N. M. Trieu dan N. T. Thinh, “Mango Panicle Stage Classification Using YOLOv3-Rotated, MangoYOLO, and R²CNN,” Agronomy, vol. 10, no. 1, pp. 143, 2021.
S. Srivastava et al., “Mango Varieties Classification-Based Optimization with Transfer Learning and Deep Learning Approaches,” dalam Advances in Machine Learning and Data Science, Springer, pp. 33–44, 2022.
Hidayat, R., Nuryana, Z., Erianda, A., Alanda, A., Rasyidah, Amnur, H. (2026). Visual Analysis and Scientometric Mapping of Deep Learning Techniques for Grapevine Disease Classification. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2025. Lecture Notes in Computer Science, vol 16215. Springer, Singapore. https://doi.org/10.1007/978-981-95-3808-9_18
F. Handayani, R. Cahyani, dan E. Prasetyo, “Analisis CNN LeNet‑5 dalam Klasifikasi Daun Mangga,” Jurnal CoSciTech, vol. 6, no. 2, 2024.
S. Ali, M. A. Raza, dan H. Zafar, “Computer Vision System for Mango Fruit Defect Detection Using Deep CNN,” Foods, vol. 11, no. 21, pp. 3483, 2021.
V. A. Gurumurthy, R. Kestur, dan O. Narasipura, “Mango Tree Net – Semantic Segmentation of Mango Trees,” arXiv preprint, arXiv:1907.06915, 2019.
P. Borianne, F. Borne, dan J. Sarron, “Deep Mangoes: Detection and Cultivar Identification with Faster R‑CNN,” arXiv preprint, arXiv:1909.10939, 2019.
K. Simonyan dan A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint, arXiv:1409.1556, 2015.
A. Krizhevsky, I. Sutskever, dan G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 25, 2012