Smart Detection of Sugarcane Leaf Rust Using UAV-Based Multispectral Imaging and Mask R-CNN
Received: 1 June 2025 | Revised: 14 August 2025 and 2 October 2025 | Accepted: 5 October 2025 | Online: 8 December 2025
Corresponding author: Elly Warni
Abstract
Detecting and monitoring sugarcane leaf rust disease remains a significant challenge in precision agriculture due to the complex symptoms and changing environmental conditions. This study introduces a smart detection framework that combines Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging with a deep learning-based Mask R-CNN model to identify and segment leaf rust-infected areas on sugarcane leaves. The system was trained and validated using annotated multispectral images captured from UAV footage at altitudes of 5, 6, and 7 meters above ground. Performance evaluation, based on precision, recall, and F1 score, revealed a clear inverse relationship between detection accuracy and flight altitude. The highest F1 Score of 0.70 was recorded at 5 meters, dropping to 0.48 at 6 meters and 0.30 at 7 meters. These results demonstrate that combining Mask R-CNN with UAV-based multispectral data has strong potential for precise and scalable plant disease detection. Additionally, the study highlights key factors that impact segmentation accuracy, including image resolution determined by UAV altitude, natural shadow interference, and overlapping leaf structures. These findings support the development of UAV- and deep learning-based plant disease detection systems, especially in tropical agricultural environments, by enabling efficient, accurate, and field-adaptive monitoring of sugarcane leaf rust disease.
Keywords:
Sugarcane Leaf Rust, Plant Disease Detection, Multispectral UAV Imaging, Mask R-CNNDownloads
References
T. F. Cardoso et al., "A regional approach to determine economic, environmental and social impacts of different sugarcane production systems in Brazil," Biomass and Bioenergy, vol. 120, pp. 9–20, Jan. 2019. DOI: https://doi.org/10.1016/j.biombioe.2018.10.018
Q. Wu, A. Li, P. Zhao, H. Xia, Y. Zhang, and Y. Que, "Theory to practice: a success in breeding sugarcane variety YZ08–1609 known as the King of Sugar," Frontiers in Plant Science, vol. 15, May 2024. DOI: https://doi.org/10.3389/fpls.2024.1413108
S. D. Artikanur, Widiatmaka, Y. Setiawan, and Marimin, "An Evaluation of Possible Sugarcane Plantations Expansion Areas in Lamongan, East Java, Indonesia," Sustainability, vol. 15, no. 6, Jan. 2023, Art. no. 5390. DOI: https://doi.org/10.3390/su15065390
M. S. Camargo et al., "Potential prophylactic role of silicon against brown rust (Puccinia melanocephala) in sugarcane," European Journal of Plant Pathology, vol. 157, no. 1, pp. 77–88, May 2020. DOI: https://doi.org/10.1007/s10658-020-01982-2
R. Selvakumar and R. Viswanathan, "Sugarcane rust: changing disease dynamics and its management," Journal of Sugarcane Research, vol. 9, no. 2, pp. 97-118, 2019. DOI: https://doi.org/10.37580/JSR.2019.2.9.97-118
D. C. Tsouros, S. Bibi, and P. G. Sarigiannidis, "A Review on UAV-Based Applications for Precision Agriculture," Information, vol. 10, no. 11, Nov. 2019, Art. no. 349. DOI: https://doi.org/10.3390/info10110349
T. B. Shahi, C.-Y. Xu, A. Neupane, and W. Guo, "Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques," Remote Sensing, vol. 15, no. 9, Jan. 2023, Art. no. 2450. DOI: https://doi.org/10.3390/rs15092450
Z. Zhang and L. Zhu, "A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications," Drones, vol. 7, no. 6, June 2023, Art. no. 398. DOI: https://doi.org/10.3390/drones7060398
P. Ahmadi, S. Mansor, B. Farjad, and E. Ghaderpour, "Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma," Remote Sensing, vol. 14, no. 5, Jan. 2022, Art. no. 1239. DOI: https://doi.org/10.3390/rs14051239
C. Wang et al., "Cotton Blight Identification with Ground Framed Canopy Photo-Assisted Multispectral UAV Images," Agronomy, vol. 13, no. 5, May 2023, Art. no. 1222. DOI: https://doi.org/10.3390/agronomy13051222
K. Liao, F. Yang, H. Dang, Y. Wu, K. Luo, and G. Li, "Detection of Eucalyptus Leaf Disease with UAV Multispectral Imagery," Forests, vol. 13, no. 8, Aug. 2022, Art. no. 1322. DOI: https://doi.org/10.3390/f13081322
C. Nguyen, V. Sagan, J. Skobalski, and J. I. Severo, "Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery," Remote Sensing, vol. 15, no. 13, Jan. 2023, Art. no. 3301. DOI: https://doi.org/10.3390/rs15133301
P. Dhoundiyal, V. Sharma, S. Vats, and P. Rawat, "A Progressive Hierarchical Model for Plant Disease Diagnosis," SN Computer Science, vol. 6, no. 2, Jan. 2025, Art. no. 102. DOI: https://doi.org/10.1007/s42979-024-03582-x
P. S. Thakur, P. Khanna, T. Sheorey, and A. Ojha, "Trends in vision-based machine learning techniques for plant disease identification: A systematic review," Expert Systems with Applications, vol. 208, Dec. 2022, Art. no. 118117. DOI: https://doi.org/10.1016/j.eswa.2022.118117
M. Shoaib et al., "An advanced deep learning models-based plant disease detection: A review of recent research," Frontiers in Plant Science, vol. 14, Mar. 2023. DOI: https://doi.org/10.3389/fpls.2023.1158933
W. Ali, G. Wang, K. Ullah, M. Salman, and S. Ali, "Substation Danger Sign Detection and Recognition using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 10051–10059, Feb. 2023. DOI: https://doi.org/10.48084/etasr.5476
R. Srinivasan, R. Korah, and M. Ravichandran, "Revolutionizing Diagnostic Insights: Exploring Advanced Image Processing Techniques and Neural Networks in Traditional Indian Medicine," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19214–19220, Feb. 2025. DOI: https://doi.org/10.48084/etasr.8975
S. Bondre and D. Patil, "Crop disease identification segmentation algorithm based on Mask-RCNN," Agronomy Journal, vol. 116, no. 3, pp. 1088–1098, 2024. DOI: https://doi.org/10.1002/agj2.21387
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 Sensing, vol. 14, no. 23, Jan. 2022, Art. no. 6137. DOI: https://doi.org/10.3390/rs14236137
F. Shahoveisi, H. Taheri Gorji, S. Shahabi, S. Hosseinirad, S. Markell, and F. Vasefi, "Application of image processing and transfer learning for the detection of rust disease," Scientific Reports, vol. 13, no. 1, Mar. 2023, Art. no. 5133. DOI: https://doi.org/10.1038/s41598-023-31942-9
Downloads
How to Cite
License
Copyright (c) 2025 Elly Warni, Indrabayu, Muhammad Yusuf, Kaisya Anindya Callista Putri Kusyanto, Muhammad H. Rizal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
