Development of an Autonomous Drone-Based Irrigation Decision Support System Utilizing Image Processing and Machine Learning Techniques

Abstract

Efficient management of water resources is essential for sustaining the global food supply amidst growing populations and climate change. Traditional irrigation methods are often plagued by inefficiencies, leading to significant water wastage. This paper presents the development and validation of an autonomous drone-based irrigation system that leverages advanced image processing and machine learning techniques to optimize water usage in agriculture. The system employs standard low-cost cameras to capture high-resolution aerial images, which are processed to accurately predict the water needs of the plants and inform irrigation decisions in real-time also it can do autonomous watering by controlling the electrical water valve in the specified irrigation areas. Comprehensive field tests conducted on pepper crops demonstrate the system's ability to enhance water use efficiency and improve crop yields. By integrating state-of-the-art technologies such as TensorFlow techniques for machine lear-nig, image analysis and autonomous navigation capabilities, the proposed solution represents a significant advancement in precision agriculture. The results indicate that the autonomous drone-based irrigation system can substantially reduce water consumption while maintaining or enhancing crop productivity, thereby promoting sustainable agricultural practices.

Keywords

• Precision Agriculture
• Autonomous Drones
• Image Processing
• Machine Learning
• Irrigation Management

References

1. [1] DSI (2020). General Directorate of State Hydraulic Works. Retrieved from https://www.dsi.gov.tr/.
2. [2] Based on data from AQUASTAT (n.d.a); [Mateo-Sagasta et al. (2015)]; and [Shiklomanav(1999)]; Contributed by [Sara Marjani Zadeh(FAO)
3. [3] Ali, S., Sultan, M., Ahmad, F., Majeed, F., Ahamed, M.S., Aziz, M., Shamshiri, R.R., Sajjad, U., Khan, M.U., Farooq, M. (2023). An Overview of Smart Irrigation Management for Improving Water Productivity under Climate Change in Drylands. Agronomy; 13(8): 2113. DOI: 10.3390/agronomy13082113.
4. [4] Dong, Y. (2023). Irrigation Scheduling Methods: Overview and Recent Advances. IntechOpen. DOI: 10.5772/intechopen.100633.
5. [5] Rasmussen, J., Jacobsen, L., Bergmann, T., & Bunk, K. (2023). How much is enough in watering plants? State-of-the-art in irrigation control: Advances, challenges, and opportunities with respect to precision irrigation. Frontiers in Sustainable Food Systems; 7: 77. DOI: 10.3389/fsufs.2023.00077.
6. [6] Hunt, E.R., Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S.T., McCarty, G.W. (2018). Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sensing; 8(1): 111. DOI: 10.3390/rs8010111.
7. [7] IntechOpen. (2023). Recent Advances in Irrigation and Drainage. DOI: 10.5772/intechopen.100759.
8. [8] Lillesand, T.M., Kiefer, R.W., Chipman, J.W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley, Hoboken.

9. [9] Smith, R.J., Baillie, J.N. (2009). Precision irrigation: challenges and opportunities. Agricultural Water Management; 96(6): 835-839. DOI: 10.1016/j.agwat.2009.02.011.
10. [10] Zhang, C., Kovacs, J.M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture; 13(6): 693-712. DOI: 10.1007/s11119-012-9274-5.
11. [11] DroneKit-Python (https://github.com/dronekit/dronekit-python/)
12. [12] Mission planner software (https://ardupilot.org/planner/docs/mission-planner-overview.html)
13. [13] Gonzalez, R.C., Woods, R.E. (2002). Digital Image Processing (2nd ed.). Prentice Hall, Upper Saddle River.
14. [14] Hartley, R., Zisserman, A. (2004). Multiple View Geometry in Computer Vision (2nd ed.). Cambridge University Press, Cambridge.
15. [15] Schott, J.R. (2007). Remote Sensing: The Image Chain Approach (2nd ed.). Oxford University Press, New York.
16. [16] Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment; 80(1): 76-87. DOI: 10.1016/S0034-4257(01)00289-9.
17. [17] LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature; 521(7553): 436-444. DOI: 10.1038/nature14539.
18. [18] Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems; 25: 1097-1105. DOI: 10.1145/3065386.
19. [19] Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... Kudlur, M. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) (pp. 265-283). DOI: 10.5555/3026877.3026899.
20. [20] TensorFlow. (n.d.). Retrieved from https://www.tensorflow.org/
21. [21] Pan, S.J., Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering; 22(10): 1345-1359. DOI: 10.1109/TKDE.2009.191.
22. [22] Yosinski, J., Clune, J., Bengio, Y., Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems; 27: 3320-3328. DOI: 10.5555/2969033.2969197.
23. [23] Shorten, C., Khoshgoftaar, T.M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data; 6(1): 1-48. DOI: 10.1186/s40537-019-0197-0.
24. [24] Chollet, F. (2015). Keras: Deep Learning for humans. Retrieved from https://keras.io/
25. [25] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research; 12: 2825-2830.