INFORMATION AND INSTRUMENTAL SUPPORT OF SITE-SPECIFIC MANAGEMENT FOR AGROCHEMICALS
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Abstract
Precision farming technologies can significantly improve the production quality and volume, while reducing the negative impact on the environment. This is achieved through the application of optimal agrochemicals doses in each elementary field zone through the joint use of remote sensing data, agrochemical, weather data, etc., as well as through the modernization of agricultural machinery (the use of an on-board computer, sensors, parallel driving systems, etc.). The aim of the study was to develop a set of approaches for information and instrumental support of site-specific management using native ready-made solutions, as well as open libraries and programs. The object of the study are two experimental agricultural fields, which are part of the Agrophysical Institute biopolygon (growing crops - cereals). Experiments have been carried out since 2019 using the native unmanned aerial system Geoscan-401. The result of the information support of agricultural technology is a specialized task map, which is loaded into the on-board computer of agricultural machinery for the subsequent application of a certain nitrogen dose in each elementary zone. The map is built on the basis of the allocation of homogeneous technological zones by methods of controlled classification. At the final stage of the implementation of the proposed approach, an important component is the parallel driving system, which allows the most efficient coverage of the territory (without passes, without entering the same zone twice). AgOpenGPS open technology with free software was chosen as the basis for creating a prototype of an analog parallel driving system. Since 2019, more than 20,000 source images have been collected and processed for the presented experimental agricultural fields. As a result, more than 20 multilayer orthophotomaps were generated to apply machine learning methods to identify homogeneous technological zones. The proposed approach for information support of differentiated technologies has been worked out, and a prototype of the native analogue of the parallel driving system has been implemented and tested.
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2. Friha O., Ferrag M. A., Shu L., Maglaras L. A., Wang X. Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies // IEEE CAA Journal of Automatica Sinica. 2021. Vol. 8, iss. 4. P. 718-752.
3. Chen N., Zhang X., Wang C. Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring // Computers and Electronics in Agriculture. 2015. Vol. 111. P. 78-91.
4. Chiu M. T., Xu X., Wei Y., Huang Z., Schwing A., Brunner R., Khachatrian H., Karapetyan H., Dozier I., Rose G., Wilson D., Tudor A., Hovakimyan N., Huang T. S., Shi H. Agriculture-Vision: A large aerial image database for agricultural pattern analysis // Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2020-Virtual, Online, United States. 2020. P. 2825-2835.
5. Segarra J., Rezzouk F. Z., Aparicio N., Gonzalez-Torralba J., Aranjuelo I., Gracia-Romero A., Araus J. L., Kefauver S. C. Multiscale assessment of ground, aerial and satellite spectral data for monitoring wheat grain nitrogen content // Information Processing in Agriculture. 2022. In Press, Corrected Proof. https://doi.org/10.1016/j.inpa.2022.05.004
6. Drechsel P., Heffer P., Magen H., Mikkelsen R., Wichelns D. (Eds.). Managing water and fertilizer for sustainable agricultural intensification. – Paris, France: IFA, IWMI, IPNI and IPI. 2015. 270 p.
7. Zhu W., Rezaei E. E., Nouri H., Sun Z., Li J., Yu D., Siebert S. UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases // Field Crops Research. 2022. Vol. 284. 108582.
8. Tsouros D. C., Bibi S., Sarigiannidis P. G. A review on UAV-based applications for precision agriculture // Information. 2019. Vol. 10, iss. 11. P. 349.
9. Belcore E., Angeli S., Colucci E., Musci M. A., Aicardi I. Precision agriculture workflow, from data collection to data management using FOSS tools: An application in Northern Italy vineyard // International Journal of Geo-Information. 2021. Vol. 10, no. 4. P. 236.
10. Lu J., Tan L., Jiang H. Review on convolutional neural network (CNN) applied to plant leaf disease classification // Agriculture. 2021. Vol. 11. P. 707.
11. Jung J., Maeda M., Chang A., Bhandari M., Ashapure A., Landivar-Bowles J. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems // Current Opinion in Biotechnology. 2021. Vol. 70. P. 15-22.
12. Yang Y., Zhang G., Chen Z., Wen X., Cheng S., Ma Q., Qi J., Zhou Y., Chen L. An independent steering driving system to realize headland turning of unmanned tractors // Computers and Electronics in Agriculture. 2022. Vol. 201. 107278.
13. Amiama-Ares C., Bueno-Lema J., Alvarez-Lopez C. J., Riveiro-Valino J. A. Manual GPS guidance system for agricultural vehicles // Spanish Journal of Agricultural Research. 2011. Vol. 9, no. 3. P. 702-712.
14. Polishchuk Yu. V., Astafiev V. L., Derepaskin A. I., Kostyuchenkov N. V., Laptev N. V., Komarov A. P. Influence of automatic and parallel driving systems on the efficiency of using machine-tractor units in the Northern region of the Republic of Kazakhstan // Natural Volatiles and Essential Oils. 2021. Vol. 8, no. 4. P. 2083-2096.
15. Zhunisbekov P. J., Matkerimov T. Y., Solntsev A. A., Temirbekov Zh. T. Comparative levels of autopilotation of transportation and technological machines // Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex. 2021. P. 1-4.
16. Lee D.-H., Kim Y.-J., Choi C.-H., Chung S.-O., Inoue E., Okayasu T. Development of a parallel hybrid system for agricultural tractors // Journal of the Faculty of Agriculture, Kyushu University. 2017. Vol. 62, no. 1. P. 137-144.
17. Mocera F. A model-based design approach for a parallel hybrid electric tractor energy management strategy using hardware in the loop technique // Vehicles. 2021. Vol. 3. P. 1-19.
18. Backman J., Oksanen T., Visala A. Parallel quidance system for tractor-trailer system with active joint // Precision Agriculture, Wageningen, the Netherlands. 2009. P. 615-622.
19. Ju C., Kim J., Seol J., Son H. I. A review on multirobot systems in agriculture // Computers and Electronics in Agriculture. 2022. Vol. 202. 107336.
20. Mitrofanova O., Yakushev V., Zakharova E., Terleev V. An alternative approach to managing the nitrogen content of cereal crops // Robotics, Machinery and Engineering Technology for Precision Agriculture. Proceedings of XIV International Scientific Conference “INTERAGROMASH 2021”, "Smart Innovation, Systems and Technologies", Singapore. 2022. P. 481-491.
21. Bure V. M., Mitrofanov E. P., Mitrofanova O. A., Petrushin A. F. Vydelenie odnorodnykh zon sel'skokhozyaistvennogo polya dlya zakladki opytov s pomoshch'yu bespilotnogo letatel'nogo apparata // Vestnik Sankt-Peterburgskogo universiteta. Prikladnaya matematika. Informatika. Protsessy upravleniya. 2018. T. 14, № 2. S. 145-150.
22. Tischler B. AgOpenGPS. https://github.com/farmerbriantee/AgOpenGPS