Model Deep Learning Hybrid CNN-AE untuk Klasifikasi Presisi Warna Buah Melon
DOI:
https://doi.org/10.52158/d3hydf28Keywords:
Deep Learning, CNN, Attention Enhancement, klasifikasi warna, buah melonAbstract
Melon fruit color classification is a critical step in assessing fruit ripeness and quality. This study proposes a hybrid deep learning model that integrates Convolutional Neural Network (CNN) and Attention Enhancement (AE) for accurate classification of melon fruit color. The model leverages CNN’s strength in visual feature extraction while enhancing focus on crucial image regions through the attention mechanism. A diverse image dataset of melon fruits was collected under various lighting conditions and angles. Pre-processing steps, including data augmentation, normalization, and image scaling, were applied to improve model generalization. The CNN-Attention hybrid architecture incorporates an attention module into the CNN layers to emphasize significant features. Comparative experiments between the standard CNN and the hybrid model demonstrate that the latter achieves superior classification accuracy, with an average improvement of 5%. Moreover, the hybrid model exhibits better robustness against image noise and lighting variations. These results indicate that incorporating Attention Enhancement can yield a more adaptive and reliable model for melon fruit color classification. The proposed approach is expected to support the development of automated systems for fruit sorting in agriculture and distribution, enhancing speed, accuracy, and efficiency for farmers, traders, and consumers.
References
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[1] M. R. Naroui Rad, “Melon Selection for Breeding Based on Traits and Diversity,” Current Agriculture Research Journal, vol. 10, no. 2, pp. 39–45, Sep. 2022, doi: 10.12944/carj.10.2.01.
[2] M. Pyingkodi, K. Thenmozhi, M. Karthikeyan, K. Chitra, N. R. W. Blessing, and S. Kumar, “Fruits Quality Detection using Deep Learning Models: A Meta- Analysis,” in 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), 2022, pp. 1–8. doi: 10.1109/ICESC54411.2022.9885289.
[3] R. Ripardo Calixto, L. G. Pinheiro Neto, T. da Silveira Cavalcante, F. G. Nascimento Lopes, A. Ripardo de Alexandria, and E. de Oliveira Silva, “Development of a computer vision approach as a useful tool to assist producers in harvesting yellow melon in northeastern Brazil,” Comput Electron Agric, vol. 192, p. 106554, 2022, doi: https://doi.org/10.1016/j.compag.2021.106554.
[4] R. Raut, A. Jadhav, C. Sorte, and A. Chaudhari, “Classification of Fruits using Convolutional Neural Networks,” in 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2022, pp. 1–4. doi: 10.1109/ICAECT54875.2022.9808070.
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[6] D. Ayala-Niño and J. M. González-Camacho, “EVALUATION OF MACHINE LEARNING MODELS TO IDENTIFY PEACH VARIETIES BASED ON LEAF COLOR,” Agrociencia, vol. 56, no. 4, pp. 669–703, Jan. 2022, doi: 10.47163/agrociencia.v56i4.2810.
[7] J. Chen, Y. Hu, J. Wang, H. Hu, and H. Cui, “Combined Effect of Ozone Treatment and Modified Atmosphere Packaging on Antioxidant Defense System of Fresh-Cut Green Peppers: ENHANCING ANTIOXIDANT DEFENSE SYSTEM,” J Food Process Preserv, vol. 40, Apr. 2016, doi: 10.1111/jfpp.12695.
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[11] M. A. Shehab, A. Al-Gizi, and S. M. Swadi, “Efficient Real-Time Object Detection based on Convolutional Neural Network,” in 2021 International Conference on Applied and Theoretical Electricity (ICATE), 2021, pp. 1–5. doi: 10.1109/ICATE49685.2021.9465015.
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[13] D. N. V. S. L. S. Indira, J. Goddu, B. Indraja, V. M. L. Challa, and B. Manasa, “A review on fruit recognition and feature evaluation using CNN,” Mater Today Proc, vol. 80, pp. 3438–3443, 2023, doi: https://doi.org/10.1016/j.matpr.2021.07.267.
[14] A. A. Alfa, S. Misra, A. Yusuf, and A. Agrawal, “Comparative Analysis of Performances of Convolutional Neural Networks for Image Classification Tasks,” in Proceedings of International Conference on Recent Innovations in Computing, Y. Singh, P. K. Singh, M. H. Kolekar, A. K. Kar, and P. J. S. Gonçalves, Eds., Singapore: Springer Nature Singapore, 2023, pp. 179–190.
[15] Dr. M. G, S. M. Gajapaka, and L. Baru, A Simple Attention Block Embedded in Standard CNN for Image Classification. 2022. doi: 10.1109/ICAAIC53929.2022.9793110.
[16] S. Zeeshan, T. Aized, and F. Riaz, “The Design and Evaluation of an Orange-Fruit Detection Model in a Dynamic Environment Using a Convolutional Neural Network,” Sustainability, vol. 15, p. 4329, Feb. 2023, doi: 10.3390/su15054329.
[17] M. Reyad, A. M. Sarhan, and M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Comput Appl, vol. 35, no. 23, pp. 17095–17112, Aug. 2023, doi: 10.1007/s00521-023-08568-z.
[18] J. Wu, Y. Liu, Y. Zhu, and Z. Li, “Atrous residual convolutional neural network based on U-Net for retinal vessel segmentation,” PLoS One, vol. 17, no. 8 August, Aug. 2022, doi: 10.1371/journal.pone.0273318.
[19] W. Geng, “Attention-Aware CNN for Fruit Image Classification,” Highlights in Science, Engineering and Technology, vol. 38, pp. 199–207, Mar. 2023, doi: 10.54097/hset.v38i.5806.
[20] Y. Yang et al., “Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data,” Feb. 2022, [Online]. Available: http://arxiv.org/abs/2202.02842
[21] F. Maleki, K. Ovens, R. Gupta, C. Reinhold, A. Spatz, and R. Forghani, “Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls.”
[22] M. Amin, “Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial,” Journal of Engineering Research, vol. 6, Dec. 2022, doi: 10.21608/erjeng.2022.274526.
[23] S. Zeng et al., “Composition design of fullerene-based hybrid electron transport layer for efficient and stable wide-bandgap perovskite solar cells,” Journal of Energy Chemistry, vol. 102, pp. 172–178, 2025, doi: https://doi.org/10.1016/j.jechem.2024.10.046.
[2] M. Pyingkodi, K. Thenmozhi, M. Karthikeyan, K. Chitra, N. R. W. Blessing, and S. Kumar, “Fruits Quality Detection using Deep Learning Models: A Meta- Analysis,” in 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), 2022, pp. 1–8. doi: 10.1109/ICESC54411.2022.9885289.
[3] R. Ripardo Calixto, L. G. Pinheiro Neto, T. da Silveira Cavalcante, F. G. Nascimento Lopes, A. Ripardo de Alexandria, and E. de Oliveira Silva, “Development of a computer vision approach as a useful tool to assist producers in harvesting yellow melon in northeastern Brazil,” Comput Electron Agric, vol. 192, p. 106554, 2022, doi: https://doi.org/10.1016/j.compag.2021.106554.
[4] R. Raut, A. Jadhav, C. Sorte, and A. Chaudhari, “Classification of Fruits using Convolutional Neural Networks,” in 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2022, pp. 1–4. doi: 10.1109/ICAECT54875.2022.9808070.
[5] R. Siddiqi, “Comparative Performance of Various Deep Learning based Models in Fruit Image Classification,” in Proceedings of the 11th International Conference on Advances in Information Technology, in IAIT ’20. New York, NY, USA: Association for Computing Machinery, 2020. doi: 10.1145/3406601.3406619.
[1] M. R. Naroui Rad, “Melon Selection for Breeding Based on Traits and Diversity,” Current Agriculture Research Journal, vol. 10, no. 2, pp. 39–45, Sep. 2022, doi: 10.12944/carj.10.2.01.
[2] M. Pyingkodi, K. Thenmozhi, M. Karthikeyan, K. Chitra, N. R. W. Blessing, and S. Kumar, “Fruits Quality Detection using Deep Learning Models: A Meta- Analysis,” in 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), 2022, pp. 1–8. doi: 10.1109/ICESC54411.2022.9885289.
[3] R. Ripardo Calixto, L. G. Pinheiro Neto, T. da Silveira Cavalcante, F. G. Nascimento Lopes, A. Ripardo de Alexandria, and E. de Oliveira Silva, “Development of a computer vision approach as a useful tool to assist producers in harvesting yellow melon in northeastern Brazil,” Comput Electron Agric, vol. 192, p. 106554, 2022, doi: https://doi.org/10.1016/j.compag.2021.106554.
[4] R. Raut, A. Jadhav, C. Sorte, and A. Chaudhari, “Classification of Fruits using Convolutional Neural Networks,” in 2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2022, pp. 1–4. doi: 10.1109/ICAECT54875.2022.9808070.
[5] R. Siddiqi, “Comparative Performance of Various Deep Learning based Models in Fruit Image Classification,” in Proceedings of the 11th International Conference on Advances in Information Technology, in IAIT ’20. New York, NY, USA: Association for Computing Machinery, 2020. doi: 10.1145/3406601.3406619.
[6] D. Ayala-Niño and J. M. González-Camacho, “EVALUATION OF MACHINE LEARNING MODELS TO IDENTIFY PEACH VARIETIES BASED ON LEAF COLOR,” Agrociencia, vol. 56, no. 4, pp. 669–703, Jan. 2022, doi: 10.47163/agrociencia.v56i4.2810.
[7] J. Chen, Y. Hu, J. Wang, H. Hu, and H. Cui, “Combined Effect of Ozone Treatment and Modified Atmosphere Packaging on Antioxidant Defense System of Fresh-Cut Green Peppers: ENHANCING ANTIOXIDANT DEFENSE SYSTEM,” J Food Process Preserv, vol. 40, Apr. 2016, doi: 10.1111/jfpp.12695.
[8] H. Gill and B. Khehra, “Hybrid classifier model for fruit classification,” Multimed Tools Appl, vol. 80, pp. 1–36, Jul. 2021, doi: 10.1007/s11042-021-10772-9.
[9] B. Noune, P. Jones, D. Justus, D. Masters, and C. Luschi, “8-bit Numerical Formats for Deep Neural Networks,” Jun. 2022, [Online]. Available: http://arxiv.org/abs/2206.02915
[10] Y. Ma et al., “High-Entropy Approach vs. Traditional Doping Strategy for Layered Oxide Cathodes in Alkali-Metal-Ion Batteries: A Comparative Study,” Energy Storage Mater, vol. 79, p. 104295, 2025, doi: https://doi.org/10.1016/j.ensm.2025.104295.
[11] M. A. Shehab, A. Al-Gizi, and S. M. Swadi, “Efficient Real-Time Object Detection based on Convolutional Neural Network,” in 2021 International Conference on Applied and Theoretical Electricity (ICATE), 2021, pp. 1–5. doi: 10.1109/ICATE49685.2021.9465015.
[12] J. Jin, “Convolutional Neural Networks for Biometrics Applications,” SHS Web of Conferences, vol. 144, p. 03013, 2022, doi: 10.1051/shsconf/202214403013.
[13] D. N. V. S. L. S. Indira, J. Goddu, B. Indraja, V. M. L. Challa, and B. Manasa, “A review on fruit recognition and feature evaluation using CNN,” Mater Today Proc, vol. 80, pp. 3438–3443, 2023, doi: https://doi.org/10.1016/j.matpr.2021.07.267.
[14] A. A. Alfa, S. Misra, A. Yusuf, and A. Agrawal, “Comparative Analysis of Performances of Convolutional Neural Networks for Image Classification Tasks,” in Proceedings of International Conference on Recent Innovations in Computing, Y. Singh, P. K. Singh, M. H. Kolekar, A. K. Kar, and P. J. S. Gonçalves, Eds., Singapore: Springer Nature Singapore, 2023, pp. 179–190.
[15] Dr. M. G, S. M. Gajapaka, and L. Baru, A Simple Attention Block Embedded in Standard CNN for Image Classification. 2022. doi: 10.1109/ICAAIC53929.2022.9793110.
[16] S. Zeeshan, T. Aized, and F. Riaz, “The Design and Evaluation of an Orange-Fruit Detection Model in a Dynamic Environment Using a Convolutional Neural Network,” Sustainability, vol. 15, p. 4329, Feb. 2023, doi: 10.3390/su15054329.
[17] M. Reyad, A. M. Sarhan, and M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Comput Appl, vol. 35, no. 23, pp. 17095–17112, Aug. 2023, doi: 10.1007/s00521-023-08568-z.
[18] J. Wu, Y. Liu, Y. Zhu, and Z. Li, “Atrous residual convolutional neural network based on U-Net for retinal vessel segmentation,” PLoS One, vol. 17, no. 8 August, Aug. 2022, doi: 10.1371/journal.pone.0273318.
[19] W. Geng, “Attention-Aware CNN for Fruit Image Classification,” Highlights in Science, Engineering and Technology, vol. 38, pp. 199–207, Mar. 2023, doi: 10.54097/hset.v38i.5806.
[20] Y. Yang et al., “Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data,” Feb. 2022, [Online]. Available: http://arxiv.org/abs/2202.02842
[21] F. Maleki, K. Ovens, R. Gupta, C. Reinhold, A. Spatz, and R. Forghani, “Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls.”
[22] M. Amin, “Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial,” Journal of Engineering Research, vol. 6, Dec. 2022, doi: 10.21608/erjeng.2022.274526.
[23] S. Zeng et al., “Composition design of fullerene-based hybrid electron transport layer for efficient and stable wide-bandgap perovskite solar cells,” Journal of Energy Chemistry, vol. 102, pp. 172–178, 2025, doi: https://doi.org/10.1016/j.jechem.2024.10.046.
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2025-12-31
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Copyright (c) 2025 Yurni Oktarina, Tresna Dewi, Dini Septiyani AR
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Model Deep Learning Hybrid CNN-AE untuk Klasifikasi Presisi Warna Buah Melon. (2025). Journal of Applied Smart Electrical Network and Systems, 6(2), 139-146. https://doi.org/10.52158/d3hydf28
