Research on Intelligent Recognition and Location Method of Crop Diseases Based on Multi-spectral Images of Unmanned Aerial

Xiaofei Sun; Joan P. Lazaro

doi:10.62677/IJETAA.2603144

Authors

Xiaofei Sun Graduate School, University of the East, Manila, Philippines Author
Joan P. Lazaro University of the East, Manila, Philippines Author

DOI:

https://doi.org/10.62677/IJETAA.2603144

Keywords:

Deep learning, Multispectral Imaging Crop Diseases, Precise Positioning, Unmanned Aerial Vehicle

Abstract

This paper studies the intelligent identification and location method of crop diseases based on multispectral images of unmanned aerial vehicles. With the development of precision agriculture, traditional crop disease monitoring methods have become difficult to meet the demands of large-scale, high-efficiency and early warning. The article first constructs a multispectral image dataset including visible light, near-infrared and red-edge bands, covering common types of crop diseases. Subsequently, an improved deep learning network architecture was proposed. The attention mechanism was adopted to enhance the model's ability to extract disease features, and a multi-scale feature fusion strategy was introduced to handle disease spots of different sizes. The research designed a data augmentation method based on spectral-spatial joint optimization, which effectively solved the problem of unbalanced samples of crop diseases. To improve positioning accuracy, this paper proposes a disease area positioning algorithm combined with geographic information system, achieving centimeter-level positioning accuracy. The experimental results show that the proposed method improves the accuracy of disease identification by 15.3% compared with the traditional methods, reduces the positioning error to an average of 3.2 centimeters, and can maintain high stability in complex field environments. In addition, this paper has established a complete technical system covering data collection, disease identification and information visualization, and has conducted application verification on crops such as wheat and rice. It has been confirmed that this method can effectively support precise pesticide application decisions in agricultural production and has significant economic and ecological benefits

Downloads

Download data is not yet available.

References

X. Li, Z. Liang, G. Yang, T. Lin, and B. Liu, "Assessing the severity of Verticillium wilt in cotton fields and constructing pesticide application prescription maps using unmanned aerial vehicle (UAV) multispectral images," Drones, vol. 8, no. 5, p. 176, 2024. https://doi.org/10.3390/drones8050176

C. Wang, X. Wang, Z. Jin et al., "Occurrence of crop pests and diseases has largely increased in China since 1970," Nature Food, vol. 3, no. 1, pp. 57–65, Jan. 2022. https://doi.org/10.1038/s43016-021-00428-0

A. Kamilaris and F. X. Prenafeta-Boldú, "Deep learning in agriculture: A survey," Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018. https://doi.org/10.1016/j.compag.2018.02.016

J. Cheng, C. Deng, Y. Su, Z. An, and Q. Wang, "Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 211, pp. 1–34, 2024. https://doi.org/10.1016/j.isprsjprs.2024.03.012

A. Caroppo, G. Diraco, and A. Leone, "A systematic review of available multispectral UAV image datasets for precision agriculture applications," Remote Sensing, vol. 18, no. 4, p. 659, 2026. https://doi.org/10.3390/rs18040659

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, p. 2450, 2023. https://doi.org/10.3390/rs15092450

R. Van De Vijver, K. Mertens, K. Heungens, D. Nuyttens, J. Wieme, W. H. Maes, J. Van Beek, B. Somers, and W. Saeys, "Ultra-high-resolution UAV-based detection of Alternaria solani infections in potato fields," Remote Sensing, vol. 14, no. 24, p. 6232, 2022. https://doi.org/10.3390/rs14246232

A. I. Gálvez-Gutiérrez, F. Afonso, and J. M. Martínez-Heredia, "On the usage of deep learning techniques for Unmanned Aerial Vehicle-based citrus crop health assessment," Remote Sensing, vol. 17, no. 13, p. 2253, 2025. https://doi.org/10.3390/rs17132253

M. G. Selvaraj, A. Vergara, F. Montenegro, H. A. Ruiz, N. Safari, D. Raymaekers, W. Ocimati, J. Ntamwira, L. Tits, A. B. Omondi, and G. Blomme, "Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 169, pp. 110–124, 2020. https://doi.org/10.1016/j.isprsjprs.2020.08.025

Z. Wang, M. A. Akber, and A. A. Aziz, "Application of remote sensing and geographic information systems for monitoring and managing chili crops: A systematic review," Remote Sensing, vol. 17, no. 16, p. 2827, 2025. https://doi.org/10.3390/rs17162827

J. Shen, H. Wang, and H. Tareque, "Toward resilience in broadacre agriculture: A methodological review of remote sensing in crop productivity, phenology, and environmental stress detection," Remote Sensing, vol. 17, no. 23, p. 3886, 2025. https://doi.org/10.3390/rs17233886

H. Yao, J. Zhu, Y. Li, H. Yan, W. Feng, L. Niu, and Z. Wu, "MAF-RecNet: A lightweight wheat and corn recognition model integrating multiple attention mechanisms," Remote Sensing, vol. 18, no. 3, p. 497, 2026. https://doi.org/10.3390/rs18030497

S. Ahmed, S. N. K. Marwat, G. B. Brahim et al., "IoT based intelligent pest management system for precision agriculture," Scientific Reports, vol. 14, p. 31917, 2024. https://doi.org/10.1038/s41598-024-83012-3

A. Goyal and K. Lakhwani, "Integrating advanced deep learning techniques for enhanced detection and classification of citrus leaf and fruit diseases," Scientific Reports, vol. 15, p. 12659, 2025. https://doi.org/10.1038/s41598-025-97159-0

X. Chen, T. Chen, H. Meng, Z. Zhang, D. Wang, J. Sun, and J. Wang, "An improved algorithm based on YOLOv5 for detecting Ambrosia trifida in UAV images," Frontiers in Plant Science, vol. 15, p. 1360419, 2024. https://doi.org/10.3389/fpls.2024.1360419

J. Sharma, A. A. Al-Huqail, A. Almogren et al., "Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures," Scientific Reports, vol. 15, p. 13904, 2025. https://doi.org/10.1038/s41598-025-98015-x

S. Maruthai, R. Selvanarayanan, T. Thanarajan et al., "Hybrid vision GNNs based early detection and protection against pest diseases in coffee plants," Scientific Reports, vol. 15, p. 11778, 2025. https://doi.org/10.1038/s41598-025-96523-4

J. Gao, K. Gujarati, M. Hegde, P. Arra, S. Gupta, and N. Buch, "Integration of UAV and remote sensing data for early diagnosis and severity mapping of diseases in maize crop through deep learning and reinforcement learning," Remote Sensing, vol. 17, no. 20, p. 3427, 2025. https://doi.org/10.3390/rs17203427

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 4700–4708. https://doi.org/10.1109/CVPR.2017.243

M. Tan and Q. V. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," in Proc. 36th Int. Conf. Machine Learning (ICML), Long Beach, CA, USA, Jun. 2019, pp. 6105–6114. https://proceedings.mlr.press/v97/tan19a.html