Authors

Tran Ngoc Huyen Trang 1,2,3* , Le Van Trung 1,2 , Vo Le Phu 1,2

Affiliations

1 Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT); tnhtrang.sdh222@hcmut.edu.vn; volephu@hcmut.edu.vn; lvtrungbk@gmail.com

2 Vietnam National University Ho Chi Minh City (VNU-HCM);

tnhtrang.sdh222@hcmut.edu.vn; volephu@hcmut.edu.vn; lvtrungbk@gmail.com

3 Department of Geodesy, Cartography and GIS, Ho Chi Minh City University of Natural Resources and Environment; tnhtrang.sdh222@hcmut.edu.vn

*Corresponding author: tnhtrang.sdh222@hcmut.edu.vn; Tel.: +84–98615481

Abstracts

Worldwide, the utilization of Unmanned Aerial Vehicles (UAVs) has been deployed in a wide range of resources management practices. The UAVs serve as valuable and visible tools for managing water resources, forests, agriculture, and land use change. Undoubtedly, the application of UAVs surpasses traditional methods in terms of efficiency, offering significant time and cost savings. Meanwhile, Artificial intelligence (AI) has emerged as a critical technology in the realm of information technology, particularly when it comes to image segmentation. The purpose of this study is to integrate UAVs and AI for mapping shrimp farms in Long An province, Mekong Delta. By leveraging AI, we empower systems to learn intricate image features and subsequently identify and segment objects within those images. In the context of modern agricultural management practices, we leverage UAV imagery as input data for AI systems to identify shrimp ponds, the image recognition platform is Deep Learning (DL) based on U – Net structure. Using the shrimp pond boundary on the 1:1000 scale topographic map as reference data, the results of this method showed a recall of 83.3%, corresponding to a miss rate of 16.7%. The precision of the method was 85.7%, corresponding to a misidentified shrimp pond extraction rate of 14.3%. The results suggest that the combination of UAVs and AI to mapping shrimp farms can facilitate efficient monitoring and management practices for local authorities. Thus, this integration is a promising application to assist and enable agri-cultural planning and regional economic development activities.

Keywords

UAV; AI; DL; Shrimp pond map.

Cite this paper

Trang, T.N.H; Trung, L.V.; Phu, V.L. Integrating UAVs and AI for shrimp pond mapping: A case study in Can Giuoc district, Long An province, Vietnam. J. Hydro-Meteorol. 2024, 21, 22-32.

References

1. Mônica, C.S.; Paloma, de M.; Pedro, E.S.L.; Antonio, D.P. Shrimp farming in coastal Brazil: Reasons for market failure and sustainability challenges. Ocean Coastal Manage. 2011, 54(9), 658–667.

2. The Observatory of Economic Complexity (OEC). Shrimps and prawns, frozen. 2021.

3. Md. Akhtaruzzaman, K.; Md. Emran, H.; Md. Sayemul, I.; Md. Takibur, R.; Madan, M.D. Shrimp export competitiveness and its determinants: a novel dynamic ARDL simulations approach. Aquacult. Econ. Manage. 2022, 1–28.

4. Pavan, K.B.; Murali, K.G.; Pranay, P.; Ismail, M.; Aya, S. Mapping shrimp pond dynamics: A spatiotemporal study using remote sensing data and machine learning. Agri. Eng. 2023, 5, 1432–1447.

5. Sharmin, A.; Md. Sadique, R.; Shopna, A.; Shirajum, M.; Tahrima, H.B. Factors driving the shifting of land from rice to aquaculture farming in Bangladesh. J. Agribus. Rural Dev. 2023, 4(70), 369–377.

6. Ha, T.T.T.; Simon, R.B. Transformations of Vietnamese shrimp aquaculture policy: Empirical evidence from the Mekong Delta. Environ. Plann. C Gov. Policy 2010, 28(6), 1101–1119.

7. Zulkarnain, R.; Adiyana, K.; Nugroho, H.; Nugraha, B.; Thesiana, L.; Supriyono, E. Selection of intensive shrimp farming technology for small farmers with analytical hierarchy process: a case for whiteleg shrimp (Litopenaeus vannamei). IOP Conf. Ser.: Earth Environ. Sci. 2020, 404, 1–8.

8. Min-Chie, C.; Wei-Mon, Y.; Showkat, A.B.; Nen-Fu, H. Development of smart aquaculture farm management system using IoT and AI-based surrogate models. J. Agric. Food Res. 2022, 9, 1–11.

9. Rohit, C.; Vandana, M. Unmanned aerial vehicles for Internet of Things (IoT): Concepts, techniques, and applications - Chapter: Unmanned aerial vehicle (UAV): A comprehensive survey. Wiley online library, 2021.

10. Amresh, C.; Singh, R.N.; Vikas, K.R.; Saurabh, K.D.; Kavita, K. Soil science: Fundamentals to recent advances – Chapter: Introduction to drone technology for natural resource management in agriculture. Springer, 2021.

11. Nurjannah, N.; Evangelos, A.; Rajuddin, S.; Hasni, A.; Elmi, N.Z.; Agus, A.; Simon, O.; Guillaume, B.; Teruhisa, K.; Laurent, B. Precision aquaculture drone mapping of the spatial distribution of Kappaphycus alvarezii Biomass and Carrageenan. J. Remote Sens. 2023, 15(3674), 1–21.

12. Sheng-I, C.; Jih-Gau, J. UAV control based on pattern recognition in aquaculture application. Aerospace 2024, 11(302), 1–33.

13. Naomi, A.U.; Shyi-Chyi, C. A Review of unmanned system technologies with its application to aquaculture farm monitoring and management. Drones 2022, 6(12), 1–41.

14. Wei-Yi, L.; Jih-Gau, J. Application of image identification to UAV control for cage culture. Sci. Prog. 2022, 105(4), 1–38.

15. Khang, N.H.T.; Duong, N.T.T.; Thi, T.T.Q.; Tien, N.M.; Chung, T.V.; Nguyen, C.V. Research on the application of UAV technology in establishing aquaculture status maps: Experiment at Hon Yen population, Phu Yen province. Duy Tan University Sci. Technol. J. 2022, 4(53), 23–30.

16. Doi, N.T.; Xuan, N.H.; Tu, N.A.; Nghi, D.T. Application of unmanned aerial vehicles (UAV) for monitoring coastal ecosystems: a study at Hon ​​Yen Group, Phu Yen Province. National scientific and technological conference “Research on application and development of national geospatial data infrastructure: The role of modern mapping technology”. 2021, pp. 187–199.

17. Subarna, S. Analysis of artificial intelligence based image classification techniques. J. Innovative Image Process 2020, 2(1), 44–54.

18. Ritica, T.; Manekar, V.L. Artificial intelligence-based image classification techniques for hydrologic applications. Appl. Artif. Intell. 2022, 36(1), 1138–1156.

19. Xi, Z. Application of artificial intelligence recognition technology in digital image processing. Wireless Commun. Mobile Comput. 2022, 3, 1–10.

20. Aya, H.M.; Moatamed, R.; Ashraf, M.H. Image classification based deep learning: A review. Aswan University J. Sci. Technol. 2022, 2(1), 11–35.

21. Babita, P.; Devendra, P.; Brijendra, P.M.; Wasiur, R. A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions. J. King Saud Univ. Comput. Inf. Sci. 2022, 34(8), 5083–5099.

22. David, G.; Miljana, H.; Danesh, A.; April, K.; Umberto, B. A U-net convolutional neural network deep learning model application for identification of energy loss in infrared thermographic images. Appl. Energy. 2024, 360, 1–15.

23. Nahian, S.; Paheding, S.; Colin, E.; Vijay, D. U-Net and its variants for medical image segmentation: Theory and applications. IEEE Access. 2021, 9, 82031–82057.

24. Olaf, R.; Philipp, F.; Thomas, B. U-Net: Convolutional networks for biomedical image segmentation. Proceeding of Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 2015, pp. 234–241.

25. Farajzadeh, Z.; Saadatseresht, M.; Alidoost, F. Automatic building extraction from UAV-based images and DSM using deep learning. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. GeoSpatial Conf. 2022, X(4), 171–177.