journal article·2024-02-20Open access

Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools

Paula Catala-Roman, Enrique A. Navarro, Jaume Segura-Garcia, Miguel Garcia-Pineda

Applied Sciences· Volume 14· Issue 5

Abstract

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Digital Transformation in Industry · Smart Agriculture and AI

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Computer Science, ETSE-UV, Universitat de València, Av. de la Universitat, s/n, 46100 Burjassot, Spain

IRTIC Institute, Universitat de València, Av. de la Universitat, s/n, 46100 Burjassot, Spain

Funding

Spanish Ministry of Science and Innovation/Spanish Research Agency (MCIN/AEI)

References (33)

Show source-provided references
1. Araújo, S.O., Peres, R.S., Barata, J., Lidon, F., and Ramalho, J.C. (2021). Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities. Agronomy, 11.10.3390/agronomy11040667 ↗
2. Ahmad, L., and Nabi, F. (2021). Agriculture 5.0: Artificial Intelligence, IoT and Machine Learning, CRC Press.10.1201/9781003125433 ↗
3. Saiz-Rubio, V., and Rovira-Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy, 10.10.3390/agronomy10020207 ↗
4. Ahamed, T. (2023). IoT and AI in Agriculture: Self-Sufficiency in Food Production to Achieve Society 5.0 and SDG’s Globally, Springer Nature.10.1007/978-981-19-8113-5 ↗
5. IoT and agriculture data analysis for smart farm10.1016/j.compag.2018.12.011 ↗
6. Martos, V., Ahmad, A., Cartujo, P., and Ordoñez, J. (2021). Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0. Appl. Sci., 11.10.3390/app11135911 ↗
7. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications10.1109/ACCESS.2019.2953499 ↗
8. Introducing digital twins to agriculture10.1016/j.compag.2020.105942 ↗
9. Nasirahmadi, A., and Hensel, O. (2022). Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm. Sensors, 22.10.3390/s22020498 ↗
10. Fuentealba, D., Flores, C., Soto, I., Zamorano, R., and Reid, S. (2022, January 20–22). Guidelines for Digital Twins in 5G Agriculture. Proceedings of the 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal.10.1109/CSNDSP54353.2022.9907935 ↗
11. Smart agriculture and digital twins: Applications and challenges in a vision of sustainability10.1016/j.eja.2023.126809 ↗
12. Digital Twins in Agriculture: A State-of-the-art review10.1016/j.atech.2022.100094 ↗
13. Barnard, A. (2023, November 11). In the Digital Indoor Garden. Available online: https://www.siemens.com/global/en/company/stories/research-technologies/digitaltwin/digital-indoor-garden.html.
14. Branthôme, F.X. (2023, November 11). Digital Twins for Tomatoes, Food and Farming. Available online: https://www.tomatonews.com/en/digital-twins-for-tomatoes-food-and-farming_2_1096.html.
15. Alves, R.G., Souza, G., Maia, R.F., Tran, A.L.H., Kamienski, C., Soininen, J.P., Aquino, P.T., and Lima, F. (2019, January 17–20). A digital twin for smart farming. Proceedings of the 2019 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA.10.1109/GHTC46095.2019.9033075 ↗
16. 3D point cloud density-based segmentation for vine rows detection and localisation10.1016/j.compag.2022.107166 ↗
17. A Decision Support System for Urban Agriculture Using Digital Twin: A Case Study with Aquaponics10.1109/ACCESS.2021.3061722 ↗
18. Alselek, M., Alcaraz-Calero, J., Segura-Garcia, J., and Wang, Q. (2022). Water IoT Monitoring System for Aquaponics Health and Fishery Applications. Sensors, 22.10.3390/s22197679 ↗
19. Mulyani, G.S., Adhitya, Y., and Köppen, M. (2023, January 6–8). Design and Implementation of Farmer Digital Twin Control in Smart Farming. Proceedings of the International Conference on Intelligent Networking and Collaborative Systems, Chiang Mai, Thailand.10.1007/978-3-031-40971-4_49 ↗
20. AgriLoRa: A digital twin framework for smart agriculture
21. (2023, July 11). Digital Twin Solutions for Smart Farming. Available online: https://www.rdworldonline.com/rd-100-2019-winner/digital-twin-solutions-for-smart-farming/.
22. The photogrammetric potential of low-cost UAVs in forestry and agriculture
23. Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture10.1016/j.compag.2018.10.005 ↗
24. Janoušek, J., Jambor, V., Marcoň, P., Dohnal, P., Synková, H., and Fiala, P. (2021). Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops. Remote. Sens., 13.10.3390/rs13101878 ↗
25. Soil surface roughness measurement: A new fully automatic photogrammetric approach applied to agricultural bare fields10.1016/j.compag.2017.01.010 ↗
26. In-field apple size estimation using photogrammetry-derived 3D point clouds: Comparison of 4 different methods considering fruit occlusions10.1016/j.compag.2021.106343 ↗
27. Linder, W. (2003). Digital Photogrammetry, Springer.10.1007/978-3-662-06725-3 ↗
28. UAV Photogrammetry for Mapping and 3D Modeling—Current Status and Future Perspectives10.5194/isprsarchives-XXXVIII-1-C22-25-2011 ↗
29. Edemetti, F., Maiale, A., Carlini, C., D’Auria, O., Llorca, J., and Maria Tulino, A. (2022, January 14–17). Vineyard Digital Twin: Construction and characterization via UAV images—DIWINE Proof of Concept. Proceedings of the 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Belfast, UK.10.1109/WoWMoM54355.2022.00094 ↗
30. Peladarinos, N., Piromalis, D., Cheimaras, V., Tserepas, E., Munteanu, R.A., and Papageorgas, P. (2023). Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review. Sensors, 23.10.3390/s23167128 ↗
31. No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models10.1109/TCSVT.2022.3186894 ↗
32. Delgado-Vera, C., Aguirre-Munizaga, M., Jiménez-Icaza, M., Manobanda-Herrera, N., and Rodríguez-Méndez, A. (2017, January 24–27). A photogrammetry software as a tool for precision agriculture: A case study. Proceedings of the International Conference on Technologies and Innovation, Guayaquil, Ecuador.10.1007/978-3-319-67283-0_21 ↗
33. Rao, A.N. (2013). Food, Agriculture and Education: Science and Technology Education and Future Human Needs, Pergamon Press.