Development of a UAV-based crop disease detection system using deep learning algorithms to enhance precision agriculture
References
- Mgendi, G. (2024). Unlocking the potential of precision agriculture for sustainable farming. Discover Agriculture, 2(1), 87. DOI: 10.1007/s44279-024-00078-3
- Khullar, V., et al. (2025). Multiple model visual feature embedding and selection method for an efficient pest classification supporting precision agriculture. Scientific Reports, 15(1), 31791. DOI: 10.1038/s41598-025-16942-1
- Chen, X., Zhang, H., & Wong, C. U. I. (2025). Dynamic monitoring and precision fertilization decision system for agricultural soil nutrients using UAV remote sensing and GIS. Agriculture, 15(15), 1627. DOI: 10.3390/agriculture15151627
- Vahidi, M., Shafian, S., & Frame, W. H. (2025). Precision soil moisture monitoring through drone-based hyperspectral imaging and PCA-driven machine learning. Sensors, 25(3), 782. DOI: 10.3390/s25030782
- Sarkar, S., Osorio Leyton, J. M., Noa-Yarasca, E., Adhikari, K., Hajda, C. B., & Smith, D. R. (2025). Integrating remote sensing and soil features for enhanced machine learning-based corn yield prediction in the southern US. Sensors, 25(2), 543. DOI: 10.3390/s25020543
- Al-Mahbashi, M., et al. (2025). An effective approach to improving photovoltaic defect detection using the new DCD-YOLOv8s model. Scientific Reports, 15(1), 38308. DOI: 10.1038/s41598-025-22307-5
- Tan, C., et al. (2024). Detection of the infection stage of pine wilt disease and spread distance using monthly UAV-based imagery and a deep learning approach. Remote Sensing, 16(2), 364. DOI: 10.3390/rs16020364
- Mo, Y., et al. (2024). Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning. Communications Biology, 7(1), 1349. DOI: 10.1038/s42003-024-07059-8
- Cao, Y., et al. (2024). Phosphorus availability influences disease-suppressive soil microbiome through plant–microbe interactions. Microbiome, 12(1), 185. DOI: 10.1186/s40168-024-01906-w
- Awad, D. A., Masoud, H. A., & Hamad, A. (2024). Climate changes and food-borne pathogens: The impact on human health and mitigation strategy. Climatic Change, 177(6), 92. DOI: 10.1007/s10584-024-03748-9
- Tekelchal, L., Hdgot, A., Gebregiorgis, G., & Zeweld, W. (2025). Socioeconomic and management implications of earthen-dam irrigation practices in Ethiopia. Sustainable Water Resources Management, 11(5), 89. DOI: 10.1007/s40899-025-01260-1
- Hyder, U., & Talpur, M. R. H. (2024). Detection of cotton leaf disease with machine learning model. Turkish Journal of Engineering, 8(2), 380–393. DOI: 10.31127/tuje.1406755
- Rehman, A., Akhtar, N., & Alhazmi, O. H. (2025). Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models. Scientific Reports, 15(1), 22570. DOI: 10.1038/s41598-025-06985-9
- Faisal, H. M., et al. (2025). Detection of cotton crops diseases using customized deep learning model. Scientific Reports, 15(1), 10766. DOI: 10.1038/s41598-025-94636-4
- Aslam, A., Usman, S. M., Zubair, M., Yasin, A., Owais, M., & Hussain, I. (2025). Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm. PLOS ONE, 20(5), e0324293. DOI: 10.1371/journal.pone.0324293
- Vellingiri, A., Mohanasundaram, K., Tamilselvan, K. S., Maheswar, R., & Ganesh, N. (2023). Multiple sensor based human detection robots: A review. International Journal on Smart Sensing and Intelligent Systems, 16(1). DOI: 10.2478/ijssis-2023-0009
- Singh, J., Singh, A. K., & Chauhan, S. S. (2025). Enhanced edge-based steganography using image segmentation and random LSB substitution for secure data hiding. International Journal on Smart Sensing and Intelligent Systems, 18(1). DOI: 10.2478/ijssis-2025-0049
- Casas, E., Arbelo, M., Moreno-Ruiz, J. A., Hernández-Leal, P. A., & Reyes-Carlos, J. A. (2023). UAV-based disease detection in palm groves of Phoenix canariensis using machine learning and multispectral imagery. Remote Sensing, 15(14), 3584. DOI: 10.3390/rs15143584
- Fei, S., et al. (2023). UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision Agriculture, 24(1), 187–212. DOI: 10.1007/s11119-022-09938-8
- Zhu, H., Liang, S., Lin, C., He, Y., & Xu, J.-L. (2024). Using multi-sensor data fusion techniques and machine learning algorithms for improving UAV-based yield prediction of oilseed rape. Drones, 8(11), 642. DOI: 10.3390/drones8110642
- Das, B., & Raghuvanshi, C. S. (2025). Advanced UAV-based leaf disease detection: Deep radial basis function networks with multidimensional mixed attention. Multimedia Tools and Applications, 84(27), 32533–32561. DOI: 10.1007/s11042-024-20462-x
- Linero-Ramos, R., Parra-Rodríguez, C., Espinosa-Valdez, A., Gómez-Rojas, J., & Gongora, M. (2024). Assessment of dataset scalability for classification of black sigatoka in banana crops using UAV-based multispectral images and deep learning techniques. Drones, 8(9), 503. DOI: 10.3390/drones8090503
- Singh, R., Gadade, A. M., & Hussain, I. (2025). Robust real-time strawberry maturity detection using UAV-mounted deep learning for precision agriculture. BMC Plant Biology, 25(1), 1367. DOI: 10.1186/s12870-025-07246-7
- Gokeda, V., & Yalavarthi, R. (2024). Deep hybrid model for pest detection: IoT-UAV-based smart agriculture system. Journal of Phytopathology, 172(5), e13381. DOI: 10.1111/jph.13381
- Fu, H., et al. (2025). A hierarchical path planning framework of plant protection UAV based on the improved D3QN algorithm and remote sensing image. Remote Sensing, 17(15), 2704. DOI: 10.3390/rs17152704
- Bokani, A., Yadegaridehkordi, E., & Kanhere, S. S. (2025). LSTM-H: A hybrid deep learning model for accurate livestock movement prediction in UAV-based monitoring systems. Drones, 9(5), 346. DOI: 10.3390/drones9050346
- Raptis, E. K., et al. (2023). End-to-end precision agriculture UAV-based functionalities tailored to field characteristics. Journal of Intelligent & Robotic Systems, 107(2), 23. DOI: 10.1007/s10846-022-01761-7
- Alavilli, S. K., Nippatla, R. P., Kadiyala, B., Boyapati, S., & Vasamsetty, C. (2025). Unified Robotic Automation and AI-Driven Transformer-Guided Graph Neural Network with Hybrid 3D-CNN, BiLSTM, and Adaptive Neuro-Symbolic Fuzzy Decision Framework for Histological Subtype and Lymph Node-Aware Breast Cancer Prediction. International Journal of Automation and Smart Technology, 15(1). doi:10.5875/j8f74e88
- Khujamatov, H., Muksimova, S., Abdullaev, M., Cho, J., Lee, C., & Jeon, H.-S. (2025). Empowering smallholder farmers with UAV-based early cotton disease detection using AI. Drones, 9(5), 385. DOI: 10.3390/drones9050385
- Saad, M. H., & Salman, A. E. (2024). A plant disease classification using one-shot learning technique with field images. Multimedia Tools and Applications, 83(20), 58935–58960. DOI: 10.1007/s11042-023-17830-4
- Jayanthi, V., & Kanchana, M. (2025). MSNet-LNet architecture with improved deep joint segmentation for tomato plant disease classification with multi-texton and statistical features. Journal of Phytopathology, 173(3), e70088. DOI: 10.1111/jph.70088
- Patil, B. V., & Patil, P. S. (2025). IoT-enhanced meta-heuristic hybrid deep learning model for predicting cotton leaf diseases. Journal of Phytopathology, 173(2), e70058. DOI: 10.1111/jph.70058
- Yang, Z.-Y., Xia, W.-K., Chu, H.-Q., Su, W.-H., Wang, R.-F., & Wang, H. (2025). A comprehensive review of deep learning applications in cotton industry: From field monitoring to smart processing. Plants, 14(10), 1481. DOI: 10.3390/plants14101481
- Bishshash, P., Nirob, M. A. S., Shikder, M. H., & Sarower, A. (2024). SAR-CLD-2024: A comprehensive dataset for cotton leaf disease detection, 2(1). DOI: 10.17632/b3jy2p6k8w.2
- Meena, P. O. O. J. A., Gupta, P. R. I. Y. A. N. K. A., & Agarwal, D. S. (2024). Integrated GLCM Texture Features and CNN for Automated Cotton Disease Identification. Journal of Theoretical and Applied Information Technology, 102(21), 7589–7600.
- Lakshmi, M. D. (2025). Advancements in early detection of cotton leaf diseases in plants using multimodal deep learning techniques. Journal of Information Systems Engineering and Management, 10(2s), 167–175. DOI: 10.52783/jisem.v10i2s.211
- Gao, L., Ran, T., Zou, H., & Wu, H. (2025). Cotton leaf disease detection using LLM-synthetic data and DEMM-YOLO model. Agriculture, 15(15), 1712. DOI: 10.3390/agriculture15151712
DOI: https://doi.org/10.2478/ijssis-2026-0031 | Journal eISSN: 1178-5608
Language: English
Submitted on: Dec 23, 2025
Published on: May 29, 2026
Published by: Macquarie University, Australia
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2026 Rupanjal Debbarma, Aditya Sankar Sengupta, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.