A Novel Deep Learning Framework Approach For Identifying The Sugarcane Disease
Keywords:
Computer Vision, Disease Detection, Sugarcane Leaf, Convolutional Models, Transfer LearningAbstract
Agriculture plays a significant role in ensuring the survival of the global economy and the growing need for food and resources. Sugarcane is a significant crop in the world and is primarily cultivated for sugar and biofuel production. But crop diseases constantly threaten their production, and crops are notoriously difficult to diagnose in early stages, and environmental conditions and slight visual differences between healthy and diseased leaves make diagnosis a challenge. In this work, a Deep Learning (DL) based method for automatic recognition of sugarcane diseases using sugarcane leaf samples is presented. Some state-of-the-art convolutional neural network (CNN) architectures were considered, as well as a novel, custom-made CNN?model proposed in this paper. The proposed custom CNN has achieved maximum accuracy of 99%, maximum precision of 99%, maximum recall of 99%, and maximum F1-score of 99% which is higher than DenseNet (93%), EfficientNet (96%), InceptionNet (91%), MobileNet (95%) and ResNet (94%). Our results clearly indicate that the proposed model can be effective in aiding farmers in early disease detection to prevent crop loss and increase?output.
References
K. Hu, H. Li, X. Fu, and S. Zhou, “Customized Weighted Ensemble of Modified Transfer Learning Models for the Detection of Sugarcane Leaf Diseases,” Electronics 2024, Vol. 13, Page 4715, vol. 13, no. 23, p. 4715, Nov. 2024, doi: 10.3390/ELECTRONICS13234715.
D. K. Sharma, P. Singh, and A. Punhani, “Sugarcane Diseases Detection using the Improved Grey Wolf Optimization Algorithm with Convolution Neural Network,” International Journal of Experimental Research and Review, vol. 38, pp. 246–254, Apr. 2024, doi: 10.52756/IJERR.2024.V38.022.
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.
M. Thanjaivadivel, C. Gobinath, J. Vellingiri, S. Kaliraj, and J. S. Femilda Josephin, “EnConv: enhanced CNN for leaf disease classification,” Journal of Plant Diseases and Protection, vol. 132, no. 1, pp. 1–12, Feb. 2025, doi: 10.1007/S41348-024-01033-6/FIGURES/6.
S. Thite, Y. Suryawanshi, K. Patil, and P. Chumchu, “Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications,” Data Brief, vol. 53, p. 110268, Apr. 2024, doi: 10.1016/J.DIB.2024.110268.
A. K. Bagchi, M. A. Haider Chowdhury, and S. A. Fattah, “Sugarcane Disease Classification using Advanced DL Algorithms: A Comparative Study,” Proceedings - 6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024, pp. 887–892, 2024, doi: 10.1109/ICEEICT62016.2024.10534394.
K. Hu, H. Li, X. Fu, and S. Zhou, “Customized Weighted Ensemble of Modified Transfer Learning Models for the Detection of Sugarcane Leaf Diseases,” Electronics (Switzerland), vol. 13, no. 23, Dec. 2024, doi: 10.3390/ELECTRONICS13234715.
E. K. Ratnasari, M. Mentari, R. K. Dewi, and R. V. H. Ginardi, “Sugarcane leaf disease detection and severity estimation based on segmented spots image,” Proceedings of 2014 International Conference on Information, Communication Technology and System, ICTS 2014, pp. 93–98, 2014, doi: 10.1109/ICTS.2014.7010564.
S. Sidana, “Improving Sugarcane Disease Identification with L1-Regularized Transfer Learning Approach,” pp. 152–166, Oct. 2024, doi: 10.2991/978-94-6463-544-7_11.
D. Banerjee, N. Sharma, D. Upadhyay, V. Singh, and K. S. Gill, “Sugarcane Leaf Health Grading Using State-of-the-Art DL Approaches,” 2024 3rd International Conference for Innovation in Technology, INOCON 2024, 2024, doi: 10.1109/INOCON60754.2024.10511478.
D. K. Sharma, P. Singh, and A. Punhani, “Sugarcane diseases detection using optimized convolutional neural network with enhanced environmental adaptation method,” Int J Experimental Res Rev, vol. 41, pp. 55–71, 2024, doi: 10.52756/ijerr.2024.v41spl.005.
A. Narmilan, F. Gonzalez, A. S. A. Salgadoe, and K. Powell, “Detection of White Leaf Disease in Sugarcane Using Machine Learning Techniques over UAV Multispectral Images,” Drones, vol. 6, no. 9, Sep. 2022, doi: 10.3390/DRONES6090230.
E. K. Ratnasari, M. Mentari, R. K. Dewi, and R. V. H. Ginardi, “Sugarcane leaf disease detection and severity estimation based on segmented spots image,” Proceedings of 2014 International Conference on Information, Communication Technology and System, ICTS 2014, pp. 93–98, 2014, doi: 10.1109/ICTS.2014.7010564.
S. A. R. Shirazi and N. Yousaf, “A Novel Approach for Red Rot Disease Detection in Sugarcane Plants Using Transfer Learning Based VGG16 Model,” NUST Journal of Engineering Sciences, vol. 17, no. 1, pp. 1–8, Jun. 2024, doi: 10.24949/NJES.V17I1.802.
?. Kundurac?o?lu and ?. Paçal, “Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models,” Journal of Operations Intelligence, vol. 2, no. 1, pp. 321–235, Aug. 2024, doi: 10.31181/JOPI21202423.
S. Sidana, “Improving Sugarcane Disease Identification with L1-Regularized Transfer Learning Approach,” pp. 152–166, Oct. 2024, doi: 10.2991/978-94-6463-544-7_11.
J. Sun, Z. Li, F. Li, Y. Shen, Y. Qian, and T. Li, “EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment,” Agronomy 2024, Vol. 14, Page 2099, vol. 14, no. 9, p. 2099, Sep. 2024, doi: 10.3390/AGRONOMY14092099.
T. Tamilvizhi, R. Surendran, K. Anbazhagan, and K. Rajkumar, “Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification,” Math Probl Eng, vol. 2022, no. 1, p. 3452413, Jan. 2022, doi: 10.1155/2022/3452413
W. Chen, C. Ju, Y. Li, S. Hu, and X. Qiao, “Sugarcane stem node recognition in field by DL combining data expansion,” Applied Sciences (Switzerland), vol. 11, no. 18, Sep. 2021, doi: 10.3390/APP11188663.
T. Huang, R. Yang, W. Huang, Y. Huang, and X. Qiao, “Detecting sugarcane borer diseases using support vector machine,” Information Processing in Agriculture, vol. 5, no. 1, pp. 74–82, Mar. 2018, doi: 10.1016/J.INPA.2017.11.001.
?. Kundurac?o?lu and ?. Paçal, “Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models,” Journal of Operations Intelligence, vol. 2, no. 1, pp. 321–235, Aug. 2024, doi: 10.31181/JOPI21202423.
A. Narmilan, F. Gonzalez, A. S. A. Salgadoe, and K. Powell, “Detection of White Leaf Disease in Sugarcane Using Machine Learning Techniques over UAV Multispectral Images,” Drones, vol. 6, no. 9, Sep. 2022, doi: 10.3390/DRONES6090230.
S. A. R. Shirazi and N. Yousaf, “A Novel Approach for Red Rot Disease Detection in Sugarcane Plants Using Transfer Learning Based VGG16 Model,” NUST Journal of Engineering Sciences, vol. 17, no. 1, pp. 1–8, Jun. 2024, doi: 10.24949/NJES.V17I1.802.
S. Singh, R. Kumar, and G. Sharma, “DL for disease detection in sugarcane leaves,” Advances in Networks, Intelligence and Computing, pp. 478–485, May 2024, doi: 10.1201/9781003430421-47.
S. Srivastava, P. Kumar, N. Mohd, A. Singh, and F. S. Gill, “A Novel DL Framework Approach for Sugarcane Disease Detection,” SN Comput Sci, vol. 1, no. 2, pp. 1–7, Mar. 2020, doi: 10.1007/S42979-020-0094-9/METRICS.
J. Sun, Z. Li, F. Li, Y. Shen, Y. Qian, and T. Li, “EF yolov8s: A Human–Computer Collaborative Sugarcane Disease Detection Model in Complex Environment,” Agronomy 2024, Vol. 14, Page 2099, vol. 14, no. 9, p. 2099, Sep. 2024, doi: 10.3390/AGRONOMY14092099.
C. Sun, X. Zhou, M. Zhang, and A. Qin, “SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism,” Sensors (Basel), vol. 23, no. 20, Oct. 2023, doi: 10.3390/S23208529.
T. Tamilvizhi, R. Surendran, K. Anbazhagan, and K. Rajkumar, “Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification,” Math Probl Eng, vol. 2022, no. 1, p. 3452413, Jan. 2022, doi: 10.1155/2022/3452413.
M. Thalor, C. Nakwa, S. Mate, and A. Shiralkar, “Enhancing Sugarcane Disease Classification Using Transfer Learning with Convolutional Neural Networks,” International Journal of Electronics and Communication Engineering, vol. Volume 12, no. 3, pp. 151–160, Mar. 2025, doi: 10.14445/23488549/IJECE-V12I3P115.
E. K. Waters, C. C. Chen, and M. R. Azghadi, “Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging,” Oct. 2024, Accessed: Jul. 30, 2025. [Online]. Available: https://arxiv.org/pdf/2410.03141
E. K. Waters, C. C.-M. Chen, and M. R. Azghadi, “Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review,” Comput Electron Agric, vol. 229, Feb. 2024, doi: 10.1016/j.compag.2024.109686.
A. Kaur, A. S. Bisht, and K. S. Kumar, “Cultivating Health: Automated Sugarcane Disease Detection Through Deep Learning,” Proceedings - International Conference on Information Science and Communications Technologies - Applications, Trends and Opportunities, ICISCT 2024, pp. 446–451, 2024, doi: 10.1109/ICISCT64202.2024.10956375.
A. Zia, J. Zhou, and M. Olayemi, “Determining Mosaic Resilience in Sugarcane Plants using Hyperspectral Images,” Jan. 2025, Accessed: Jul. 30, 2025. [Online]. Available: https://arxiv.org/pdf/2501.16700
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