Method of Precise Irrigation and Fertilization Using a Group of Autonomous Robotic Agents

Today, climate change, the limited availability of natural resources, coupled with an increase in total consumption, constantly increase the requirements for agricultural facilities. One of the urgent tasks in the field of robotic agricultural automation systems is the task of developing methods and approaches to precise irrigation and fertilization, characterized by a high level of autonomy, a wide working area and the ability to perform tasks in a continuous mode. Thus, within this study, a method of precise fertilizer application was proposed, based on the use of a group of heterogeneous robotic means. The heterogeneous composition of the system provides the possibility of replacing batteries and replenishing the solution tanks of the robotic means, which carry out the application of fertilizers in the areas of operations through the use of specialized ground robots. Approbation of the proposed method was carried out in the Gazebo virtual environment on the example of a garden of columnar apple trees with an area of several hectares, which includes more than 8000 trees. The final consolidated assessment of the proposed solution, averaged over all selected groups of tasks, was 74.6 %. The average proportion of trees missed in the experiment was: 7.8 %. According to the results of the experiment, the proposed solution allows not only to successfully carry out the tasks of fertilizing large agricultural facilities in a continuous mode of operation, but also to carry out autonomous identification of potential zones where fertilization is required.

1. Doctrine of food security of the Russian Federation, available at: http://www.scrf.gov.ru/security/economic/document108/ (accessed 07/11/2022) (in Russian).

2. Strategy for the development of agro-industrial and fishery complexes of the Russian Federation for the period up to 2030б available at: https://docs.cntd.ru/document/564654448 (accessed 07/11/2022) (in Russian).

3. Shamshiri R., Weltzien C., Hameed I. A., Yule I., Grift T., Balasundram S. K., Chowdhary G. Research and development in agricultural robotics: A perspective of digital farming, International Journal of Agricultural and Biological Engineering, 2018, vol. 11, no. 4, pp. 1—14.

4. Bechar A., Vigneault C. Agricultural robots for field operations. Part 2: Operations and systemб Biosystems Engineering, 2017, vol. 153, pp. 110—128.

5. Guzey A., Akinci M. M., Guzey H. M. Smart Agriculture with Autonomous Unmanned Ground and Air Vehicles: Approaches to Calculating Optimal Number of Stops in Harvest Optimization and a Suggestionб In Artificial Intelligence and IoTBased Technologies for Sustainable Farming and Smart Agriculture, 2021, pp. 151—174.

6. Tsouros D. C., Bibi S., Sarigiannidis P. G. A review on UAV-based applications for precision agriculture, Information, 2019, vol. 10, no. 11, p. 349.

7. Vardhan P. H., Dheepak S., Aditya P. T., Arul S. Development of Automated Aerial Pesticide Sprayer, International Journal of Engineering Science and Research Technology, 2014, vol, no. 4.

8. ME S. M., Maguteeswaran R., BE N. G., Srinivasan G. Quadcopter UAV based fertilizer and pesticide spraying system, Int. Acad. Res. J. Eng. Sci, 2016, vol. 1, pp. 8—12.

9. Nikolaenkov A. A., Osipov E. V. Layout of an unmanned aerial vehicle for agricultural purposes, 2018, available at: http://elib.osu.ru/handle/123456789/5789 (date of access: 12.12.2022) (in Russian).

10. Baluprithviraj K. N., Naveena P., Palanisamy R. Quadcopter based Automatic Spattering of Pesticides and Fertilizers, Journal of Electronic Design Engineering, 2016, vol. 2, no. 2.

11. Yanliang Z., Qi L., Wei Z. Design, and test of a six-rotor unmanned aerial vehicle (UAV) electrostatic spraying system for crop protection, International Journal of Agricultural and Biological Engineering, 2017, vol. 10, no. 6, pp.68—76.

12. Qin W., Xue X., Zhang S., Gu W., Wang B. Droplet deposition and efficiency of fungicides sprayed with small UAV against wheat powdery mildew, International Journal of Agricultural and Biological Engineering, 2018, vol. 11, no. 2, pp. 27—32.

13. Tang Y., Hou C. J., Luo S. M., Lin J. T., Yang Z., Huang W. F. Effects of operation height and tree shape on droplet deposition in citrus trees using an unmanned aerial vehicle, Computers and Electronics in Agriculture, 2018, vol. 148, pp.1—7.

14. Area of agricultural land by category of farms. Agriculture, hunting and game management, forestry in Russia, 2011, available at: https://www.gks.ru/bgd/regl/b11_38/IssWWW.exe/Stg/04-01.htm (accessed 11.07.2022).

15. Xue J., Su B. Significant remote sensing vegetation indices: A review of developments and applications, Journal of sensors, 2017, vol. 2017.

16. Ghazal M., Al Khalil Y., Hajjdiab H. UAV-based remote sensing for vegetation cover estimation using NDVI imagery and level sets method, Signal Processing and Information Technology (ISSPIT), 2015 IEEE International Symposium on, 2015, pp. 332—337.

17. Daroya R., Ramos M. NDVI image extraction of an agricultural land using an autonomous quadcopter with a filter-modified camera, Control System, Computing and Engineering (ICCSCE), 2017 7th IEEE International Conference on, 2017, pp. 110—114.

18. Schubert E., Sander J., Ester M., Kriegel H. P., Xu X. DBSCAN revisited, revisited: why and how you should (still) use DBSCAN, ACM Transactions on Database Systems (TODS), 2017, vol. 42, no. 3, pp. 1—21.

19. Yan Z., Liang J., Pan W., Li J., Zhang C. Weakly-and semi-supervised object detection with expectation-maximization algorithm, arXiv preprint arXiv:1702.08740, 2017.