(Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)(2) Raden Achmad Chairdino Leuveano
(Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)(3) Heru Cahya Rustamaji
(Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)(4) Andrey Ferriyan
(Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia)(5) Panut Mulyono
(Universitas Gadjah Mada, Indonesia)(6) Bayu Prasetya Wijaya
(Universitas Pembangunan Nasional Veteran Yogyakarta)*corresponding author
AbstractAssessing sugarcane quality is crucial for ensuring both economic value and processing efficiency in sugar production. Conventional approaches, such as refractometer-based Brix measurements, are destructive, labor-intensive, and unsuitable for large-scale or rapid field evaluations. This study proposes a non-destructive deep learning framework for classifying sugarcane internodes into two quality categories (< 16 °Bx and ≥16 °Bx) to address existing limitations. Two convolutional neural network architectures, VGG19 and ResNet50, were evaluated utilizing a defined transfer learning and data augmentation methodology. Because of its residual connections, which enable deeper and more stable feature learning, ResNet50 consistently outperformed VGG19, achieving the highest accuracy of 78.85% on the Luar2_Putih dataset. This comparative finding demonstrates that modern residual-based networks provide superior robustness for subtle visual classification tasks in agricultural imaging, while also validating the stability of the proposed two-phase training framework. The study advances AI-driven non-destructive quality assessment by offering a scalable, field-deployable solution that supports sustainable, efficient sugarcane processing in line with the UN Sustainable Development Goals (SDG 2, 9, 12, and 13).
KeywordsSugarcane quality; Machine learning; Brix; VGG19; ResNet50
|
DOIhttps://doi.org/10.26555/ijain.v12i1.2236 |
Article metricsAbstract views : 322 | PDF views : 57 |
Cite |
Full Text Download
|
References
[1] OECD/FAO, “OECD-FAO Agricultural Outlook 2020-2029,” Rome/OECD Publishing, Paris, 2020. doi: 10.1787/1112c23b-en.
[2] International Energy Agency, “Renewables 2021: Analysis and forecast to 2026,” pp. 1- 175, 2021. [Online]. Available at: https://iea.blob.core.windows.net/assets/5ae32253-7409-4f9a-a91d-1493ffb9777a/Renewables2021-Analysisandforecastto2026.pdf.
[3] Republic of Indonesia, “Presidential Regulation No. 40 of 2023 on acceleration of national sugar self-sufficiency and provision of bioethanol as biofuel,” pp. 1-14, 2023. [Online]. Available at: https://peraturan.bpk.go.id/Details/251973/perpres-no-40-tahun-2023.
[4] Climate Action Tracker, “Indonesia: Policies and action,” 2024. [Online]. Available at: https://climateactiontracker.org/countries/indonesia/2024-12-10/policies-action/.
[5] United Nations, “Transforming our world: The 2030 Agenda for Sustainable Development,” 2015. [Online]. Available at: https://sdgs.un.org/2030agenda.
[6] South African Sugarcane Research Institute, “Determining crop maturity for purposes of cane quality management (Information Sheet 4.7),” Mount Edgecombe, South Africa, pp. 1 - 6, 2022. [Online]. Available at: https://sasri.org.za/wp-content/uploads/2022/08/4.7-Determining-crop-maturity-for-purposes-of-cane-quality-management.pdf.
[7] V. Misra, A. K. Mall, S. Solomon, and M. I. Ansari, “Post-harvest biology and recent advances of storage technologies in sugarcane,” Biotechnology Reports, vol. 33. p. e00850, 2022, doi: 10.1016/j.btre.2022.e00705.
[8] L. de P. Corrêdo, J. P. Molin, and R. Canal Filho, “Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy?,” AgriEngineering, vol. 6, no. 1, pp. 64–80, 2024, doi: 10.3390/agriengineering6010005.
[9] C. Bisaglia et al., “Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives,” Agriculture, vol. 15, no. 4, p. 377, 2025, doi: 10.3390/agriculture15040377.
[10] R. C. de Oliveira and R. D. de S. e. Silva, “Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends,” Applied Sciences (Switzerland), vol. 13, no. 13. 2023, doi: 10.3390/app13137405.
[11] A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, pp. 4843–4873, 2021, doi: 10.1109/ACCESS.2020.3048415.
[12] P. P. Ruwanpathirana et al., “Evaluation of Sugarcane Crop Growth Monitoring Using Vegetation Indices Derived from RGB-Based UAV Images and Machine Learning Models,” Agronomy, vol. 14, no. 9, p. 2059, Sep. 2024, doi: 10.3390/agronomy14092059.
[13] N. Amarasingam, F. Gonzalez, A. S. A. Salgadoe, J. Sandino, and K. Powell, “Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models,” Remote Sens., vol. 14, no. 23, p. 6137, Dec. 2022, doi: 10.3390/rs14236137.
[14] Y. Huang, R. Li, X. Wei, Z. Wang, T. Ge, and X. Qiao, “Evaluating Data Augmentation Effects on the Recognition of Sugarcane Leaf Spot,” Agric., vol. 12, no. 12, 2022, doi: 10.3390/agriculture12121997.
[15] R. Pudupet Ethiraj and K. Paranjothi, “A deep learning-based approach for early detection of disease in sugarcane plants: an explainable artificial intelligence model,” IAES Int. J. Artif. Intell., vol. 13, no. 1, p. 974, Mar. 2024, doi: 10.11591/ijai.v13.i1.pp974-983.
[16] M. R. Barbosa Júnior, B. R. de A. Moreira, R. P. de Oliveira, L. S. Shiratsuchi, and R. P. da Silva, “UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane,” Front. Plant Sci., vol. 14, pp. 1–11, Jan. 2023, doi: 10.3389/fpls.2023.1114852.
[17] T. F. Canata, M. C. F. Wei, L. F. Maldaner, and J. P. Molin, “Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique,” Remote Sens., vol. 13, no. 2, p. 232, Jan. 2021, doi: 10.3390/rs13020232.
[18] J. C. S. Vasconcelos, C. S. Arantes, E. A. Speranza, J. F. G. Antunes, L. A. F. Barbosa, and G. M. de A. Cançado, “Predicting Sugarcane Yield Through Temporal Analysis of Satellite Imagery During the Growth Phase,” Agronomy, vol. 15, no. 4, p. 793, Mar. 2025, doi: 10.3390/agronomy15040793.
[19] S. Widodo, A. Ahmar, and M. Solahudin, “Nondestructive Prediction of Brix Value in Sugarcane Based of Portable NIR Spectroscopy,” J. Keteknikan Pertan., vol. 12, no. 3, pp. 424–437, Dec. 2024, doi: 10.19028/jtep.012.3.424-437.
[20] M. V. A. da Costa, C. H. Fontes, G. Carvalho, and E. C. de M. Júnior, “UltraBrix: A Device for Measuring the Soluble Solids Content in Sugarcane,” Sustainability, vol. 13, no. 3, p. 1227, Jan. 2021, doi: 10.3390/su13031227.
[21] S. A. Jaywant, H. Singh, and K. M. Arif, “Sensors and Instruments for Brix Measurement: A Review,” Sensors, vol. 22, no. 6, p. 2290, Mar. 2022, doi: 10.3390/s22062290.
[22] U. Nawaz, M. Z. Zaheer, F. S. Khan, H. Cholakkal, S. Khan, and R. M. Anwer, “AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock,” arXiv, pp. 1–42, 2025, [Online]. Available at: https://arxiv.org/abs/2507.22101.
[23] F. Ali et al., “From Sensors to Insights: The Fusion of AI, Edge Computing, and Precision Agriculture,” J. Agric. Biol., vol. 3, no. 1, pp. 160–170, Feb. 2025, doi: 10.55627/agribiol.003.01.1067.
[24] W. B. Demilie, “Plant disease detection and classification techniques: a comparative study of the performances,” J. Big Data, vol. 11, no. 1, p. 5, Jan. 2024, doi: 10.1186/s40537-023-00863-9.
[25] H. M. Yusuf, S. A. Yusuf, A. H. Abubakar, M. Abdullahi, and I. H. Hassan, “A systematic review of deep learning techniques for rice disease recognition: Current trends and future directions,” Franklin Open, vol. 8, no. July, p. 100154, Sep. 2024, doi: 10.1016/j.fraope.2024.100154.
[26] N. Ismail and O. A. Malik, “Real-time visual inspection system for grading fruits using computer vision and deep learning techniques,” Inf. Process. Agric., vol. 9, no. 1, pp. 24–37, Mar. 2022, doi: 10.1016/j.inpa.2021.01.005.
[27] S. Sridhara, Soumya B. R., And G. R. Kashyap, “Multistage sugarcane yield prediction using machine learning algorithms,” J. Agrometeorol., vol. 26, no. 1, pp. 37–44, Mar. 2024, doi: 10.54386/jam.v26i1.2411.
[28] P. M. Kai, B. M. de Oliveira, and R. M. da Costa, “Deep Learning-Based Method for Classification of Sugarcane Varieties,” Agronomy, vol. 12, no. 11, p. 2722, Nov. 2022, doi: 10.3390/agronomy12112722.
[29] Z. Li, G. Chen, and T. Zhang, “A CNN-Transformer Hybrid Approach for Crop Classification Using Multitemporal Multisensor Images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 847–858, 2020, doi: 10.1109/JSTARS.2020.2971763.
[30] T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,” ISPRS J. Photogramm. Remote Sens., vol. 173, pp. 24–49, Mar. 2021, doi: 10.1016/j.isprsjprs.2020.12.010.
[31] M. Knott, F. Perez-Cruz, and T. Defraeye, “Facilitated machine learning for image-based fruit quality assessment,” J. Food Eng., vol. 345, p. 111401, May 2023, doi: 10.1016/j.jfoodeng.2022.111401.
[32] S. D. Daphal and S. M. Koli, “Enhancing sugarcane disease classification with ensemble deep learning: A comparative study with transfer learning techniques,” Heliyon, vol. 9, no. 8, p. e18261, Aug. 2023, doi: 10.1016/j.heliyon.2023.e18261.
[33] S. D. Daphal and S. M. Koli, “Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration,” Heliyon, vol. 10, no. 8, p. e29438, Apr. 2024, doi: 10.1016/j.heliyon.2024.e29438.
[34] S. Veeragandham and H. Santhi, “Effectiveness of convolutional layers in pre-trained models for classifying common weeds in groundnut and corn crops,” Comput. Electr. Eng., vol. 103, p. 108315, Oct. 2022, doi: 10.1016/j.compeleceng.2022.108315.
[35] N. R. de França e Silva, M. E. D. Chaves, A. C. dos S. Luciano, I. D. Sanches, C. M. de Almeida, and M. Adami, “Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review,” Remote Sens., vol. 16, no. 5, p. 863, Feb. 2024, doi: 10.3390/rs16050863.
[36] E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, Jun. 2019, doi: 10.1016/j.compag.2018.03.032.
[37] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, p. 105393, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
[38] W. Zhao, W. Yamada, T. Li, M. Digman, and T. Runge, “Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection,” Remote Sens., vol. 13, no. 1, p. 23, Dec. 2020, doi: 10.3390/rs13010023.
[39] P. Maheswari, P. Raja, and V. T. Hoang, “Intelligent yield estimation for tomato crop using SegNet with VGG19 architecture,” Sci. Rep., vol. 12, no. 1, p. 13601, Aug. 2022, doi: 10.1038/s41598-022-17840-6.
[40] S. R. Shah, S. Qadri, H. Bibi, S. M. W. Shah, M. I. Sharif, and F. Marinello, “Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease,” Agronomy, vol. 13, no. 6, p. 1633, Jun. 2023, doi: 10.3390/agronomy13061633.
[41] A. K. Trivedi, T. Mahajan, T. Maheshwari, R. Mehta, and S. Tiwari, “Leveraging feature fusion ensemble of VGG16 and ResNet-50 for automated potato leaf abnormality detection in precision agriculture,” Soft Comput., vol. 29, no. 4, pp. 2263–2277, 2025, doi: 10.1007/s00500-025-10523-0.
[42] R. Yunita, R. S. Hartati, S. Suhesti, and Syafaruddin, “Response of bululawang sugarcane variety to salt stress,” IOP Conf. Ser. Earth Environ. Sci., vol. 418, no. 1, p. 012060, Jan. 2020, doi: 10.1088/1755-1315/418/1/012060.
[43] P. Johri et al., “Advanced deep transfer learning techniques for efficient detection of cotton plant diseases,” Front. Plant Sci., vol. 15, p. 1441117, 2024, doi: 10.3389/fpls.2024.1441117.
[44] H. Ghosh et al., “Advanced neural network architectures for tomato leaf disease diagnosis in precision agriculture,” Discov. Sustain., vol. 6, no. 1, p. 312, 2025, doi: 10.1007/s43621-025-01149-1.
[45] Y. Pamungkas, E. Triandini, W. Yunanto, and Y. Thwe, “Impact of Hyperparameter Tuning on ResNet-UNet Models for Enhanced Brain Tumor Segmentation in MRI Scans,” Int. J. Robot. Control Syst., vol. 5, no. 2, pp. 917–936, Mar. 2025, doi: 10.31763/ijrcs.v5i2.1802.
[46] T. Shi, J. Yu, Z. Hu, and K. Jiang, “Improved Method of ResNet50 Image Classification Based on Transfer Learning,” Int. J. Adv. Network, Monit. Control., vol. 10, no. 2, pp. 1–9, Jun. 2025, doi: 10.2478/ijanmc-2025-0011.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0