Dual-path model for irrigation facility detection via UAV remote sensing
Keywords:
irrigation facilities, condition detection, UAV, DP-ResNet50Abstract
Accurate and efficient condition detection of irrigation facilities is crucial for ensuring stable crop production and enhancing food security. However, traditional detection methods often fail to meet the demands for refined monitoring in large-scale irrigation areas. To address this issue, this study proposed an irrigation facility condition detection system based on UAV low-altitude remote sensing and the DP-ResNet50 model to identify four common states of irrigation canal systems. First, with ResNet-50 as the backbone network, the CalibratedFocal Loss function was adopted to mitigate biases arising from imbalanced class distributions and reduce overconfident predictions in ambiguous scenarios. Second, the Dual-Path and Fusion Collaborative Module (DP-FCM) was designed to enhance the ability to capture differentiated local features, such as silt and gravel, through the complementary combination of a general backbone and a lightweight discriminative path. Finally, to further address the high-dimensional redundancy and low-dimensional confusion in irrigation canal state features, the Enhancement and Classifier Integrated Module (EC-IM) was proposed to realize more effective feature mapping and state discrimination. Experimental results demonstrate that the DP-ResNet50 model achieved favorable performance in irrigation canal condition detection, with an accuracy of 0.9413, alongside macro precision, macro recall, and macro F1-score of 0.9250, 0.9195, and 0.9211, respectively. Furthermore, compared to other classic models, the DP-ResNet50 model demonstrates higher and more balanced recall rates for individual classes. The results of this study can provide a promising solution for the intelligent monitoring of smart agricultural irrigation systems.
Key words: irrigation facilities; condition detection; UAV; DP-ResNet50
DOI: 10.25165/j.ijabe.20261904.10532
Citation: Xu F H, Jiang J W, Wang Y H, Zhou S H, Su B F. Dual-path model for irrigation facility detection via UAV remotesensing. Int J Agric & Biol Eng, 2026; 19(4): 174–183.
References
[1] Liu M H, Xu X, Jiang Y, Huang Q Z, Huo Z L, Liu L, et al. Responses of crop growth and water productivity to climate change and agricultural water-saving in arid region. Science of the Total Environment, 2020; 703: 134621.
[2] Gohar A A, Amer S A, Ward F A. Irrigation infrastructure and water appropriation rules for food security. Journal of Hydrology, 2015; 520: 85–100.
[3] Awais M, Li W, Cheema M J M, Zaman Q U, Shaheen A, Aslam B, et al. UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: A meta-review. International Journal of Environmental Science and Technology, 2023; 20(1): 1135–1152.
[4] Bhatti M T, Ashraf M, Anwar A A. Soil erosion and sediment load management strategies for sustainable irrigation in arid regions. Sustainability, 2021; 13(6): 3547.
[5] Pádua L, Marques P, Dinis L-T, Moutinho-Pereira J, Sousa J J, Morais R, et al. Detection of leak areas in vineyard irrigation systems using UAV-based data. Drones, 2024; 8(5): 187.
[6] Colomina I, Molina P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 2014; 92: 79–97.
[7] Hnatiuc M, Paun M, Kapsamun D. IoT sensors system for vineyard monitoring. In: 2022 7th International Conference on Information and Network Technologies (ICINT), 2022; pp.46–51. doi: 10.1109/ICINT55083.2022.00015.
[8] Jabbar W A, Ting T M, Hamidun M F I, Kamarudin A H C, Wu W, Sultan J, et al. Development of LoRaWAN-based IoT system for water quality monitoring in rural areas. Expert Systems with Applications, 2024; 242: 122862.
[9] Traore S, Fipp G, Swanson C, Dufek J, Robin M. Unmanned aerial vehicle system integrating high-resolution sensors for detecting leaks in irrigation canals—Proof of concept. Irrigation Drainage, 2021; 71(1): 206–217.
[10] Jalajamony H M, Nair M, Mead P F, Fernandez R E. Drone aided thermal mapping for selective irrigation of localized dry spots. IEEE Access, 2023; 11: 7320–7335.
[11] Ferro M V, Catania P, Miccichè D, Pisciotta A, Vallone M, Orlando S. Assessment of vineyard vigour and yield spatio-temporal variability based on UAV high resolution multispectral images. Biosystems Engineering, 2023; 231: 36–56.
[12] Zheng Y, Zhu Q B, Huang M, Guo Y, Qin J. Maize and weed classification using color indices with support vector data description in outdoor fields. Computers and Electronics in Agriculture, 2017; 141: 215–222.
[13] Liu T, Jin X J, Zhang L Y, Wang J, Chen Y, Hu C S, et al. Semi-supervised learning and attention mechanism for weed detection in wheat. Crop Protection, 2023; 174: 106389.
[14] Castellano G, De Marinis P, Vessio G. Weed mapping in multispectral drone imagery using lightweight vision transformers. Neurocomputing, 2023; 562: 126914.
[15] Gao J F, Nuyttens D, Lootens P, He Y, Pieters J G. Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosystems Engineering, 2018; 170: 39–50.
[16] Bakhshipour A, Jafari A. Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture, 2018; 145: 153–160.
[17] Saad El Imanni H, El Harti A, Bachaoui E M, Mouncif H, Eddassouqui F, Hasnai M A, et al. Multispectral UAV data for detection of weeds in a citrus farm using machine learning and Google Earth Engine: Case study of Morocco. Remote Sensing Applications: Society and Environment, 2023; 30: 100941.
[18] Su J Y, Yi D W, Coombes M, Liu C J, Zhai X J, McDonald-Maier K, et al. Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery. Computers and Electronics in Agriculture, 2022; 192: 106621.
[19] Zhang K, Qin L, Zhu L. PDS-YOLO: A real-time detection algorithm for pipeline defect detection. Electronics, 2025; 14(1): 208.
[20] Christiansen P, Nielsen L N, Steen K A, Jørgensen R N, Karstoft H. DeepAnomaly: Combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors, 2016; 16(11): 1904.
[21] Islam M M, Talukder M A, Sarker M R A, Uddin M A, Akhter A, Sharmin S, et al. A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intelligent Systems with Applications, 2023; 20: 200278.
[22] Aloysius N, Geetha M. A review on deep convolutional neural networks. In: 2017 International Conference on Communication and Signal Processing (ICCSP), 2017; pp.0588–0592. doi: 10.1109/ICCSP.2017.8286426
[23] Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 2022; 33(12): 6999–7019.
[24] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016; pp.770-778. doi: 10.1109/CVPR.2016.90.
[25] Upadhyay L, Saxena A. Evaluation of enhanced Resnet-50 based deep learning classifier for tomato leaf disease detection and classification. Journal of Electrical Systems, 2024; 20(3s): 1–14.
[26] Ahmed M, Ahmed A. Palm tree disease detection and classification using residual network and transfer learning of inception ResNet. PLOS One, 2023; 18(3): e0282250.
[27] Haggab O N, Al-Qaysi Z T. Detecting defect in central pivot irrigation system using YOLOv7 algorithms. Al-Salam Journal for Engineering and Technology, 2024; 3(2): 38–49.
[28] Mukhoti J, Kulharia V, Sanyal A, Golodetz S, Torr P H S, Dokania P K. Calibrating deep neural networks using focal loss. In: Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS ’20), 2020; Article 1282, pp.15288–15299.
[29] Woo S, Park J, Lee J-Y, Kweon I S. CBAM: Convolutional block attention module. In: Computer Vision -- ECCV 2018, 2018; pp.3–19. doi: 10.1007/978-3-030-01234-2_1.
[30] Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon I S, et al. ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023; pp.16133–16142. doi: 10.1109/CVPR52729.2023.01548.
[31] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint, 2021; arXiv: 2010.11929. doi: 10.48550/arXiv.2010.11929.
[32] Tu Z, Talebi H, Zhang H, Yang F, Milanfar P, Bovik A, et al. MaxViT: Multi-axis vision transformer. In: European Conference on Computer Vision (ECCV), 2022; pp.459–479. doi: 10.1007/978-3-031-20053-3_27.
[33] Tan M, Le Q V. EfficientNet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (ICML), 2019; arXiv: 195.11946. doi: 10.48550/arXiv.1905.11946.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Agricultural and Biological Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJABE is an international peer reviewed, open access journal, adopting Creative Commons Copyright Notices as follows.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal 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) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
