Intelligent Field Sensor Station for Monitoring Agrophysical Parameters and Phenotyping in Precision Agriculture System

Current trends in agriculture highlight the widespread adoption of information technology and Internet of Things (IoT) sensor networks for monitoring agrophysical soil parameters and phenotyping objects. This approach enables precise, real-time data analysis, optimizing agricultural processes and supporting the development of adaptive management systems. The integration of information technology with the monitoring of agrophysical parameters and phenotyping objects underscores the strategic importance of this approach, especially in the context of climate variability and the growing need to enhance production sustainability. (Research purpose) To develop an intelligent field sensor station for precision farming that ensures high-accuracy, real-time monitoring of agrophysical parameters and plant phenotyping using an Internet of Things sensor network. (Materials and methods) Existing methods for monitoring agrophysical parameters and phenotyping objects were analyzed. Based on these methods, a design for an intelligent field sensor station was developed, and suitable sensors were selected. (Results and discussion) The intelligent field sensor station successfully demonstrated its efficiency, confirming both its functionality and reliability in simultaneous data collection. The data collected on soil agrophysical parameters, meteorological conditions and plant phenotyping provide extensive knowledge for precision farming and optimizing agricultural processes. (Conclusions) Light gray forest soil with high porosity and neutral pH level provided favorable conditions for crops. Preliminary chemical analysis of the soil revealed moderate levels of organic matter, mobile phosphorus, and potassium, indicating a potentially fertile site. Meteorological data playeda key role in agrometeorological analysis, significantly impacting agricultural processes. The developed station introduces an innovative approach to monitoring agricultural parameters, offering promising prospects for modern agriculture.

1. Friedli M., Kirchgessner N., Grieder C. et al. Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. Plant Methods. 2016. N12. 9 (In English). DOI: 10.1186/s13007-016-0109-7.

2. Barreto B.B., Rivera F.P., McKenzie B.M. et al. Analysis of the effect of tilling and crop type on soil structure using 3D laser profilometry. Agriculture. 2023. N13. 2077 (In English). DOI: 10.3390/agriculture13112077.

3. Kazemi M., Samavati F.F. Automatic soil sampling site selection in management zones using a multi-objective optimization algorithm. Agriculture. 2023. N13. 1993 (In English). DOI: 10.3390/agriculture13101993.

4. Yue J., Zhou C., Feng H. et al. Novel applications of optical sensors and machine learning in agricultural monitoring. Agriculture. 2023. N13. 1970 (In English). DOI: 10.3390/agriculture13101970.

5. Rossi R., Costafreda-Aumedes S., Leolini L. et al. Implementation of an algorithm for automated phenotyping through plant 3D-modeling: A practical application on the early detection of water stress. Computers and Electronics in Agriculture. 2022. Vol. 197. 106937 (In English). DOI: 10.1016/j.compag.2022.106937.

6. Songhee C., Taehyeong K., Dae-Hyun J. et al. Plant growth information measurement based on object detection and image fusion using a smart farm robot. Computers and Electronics in Agriculture. 2023. Vol. 207. 107703 (In English). DOI: 10.1016/j.compag.2023.107703.

7. Zimmermann G., Jasper S., Savi D. et al. Development of an electronic profilometer to measure mobilization variables in soil harrowing. Spanish Journal of Agricultural Research. 2023. 21(2): e0204. DOI: 10.5424/sjar/2023212-19811.

8. Vasilyev A.A., Vasilyev S.A., Shkilev N.P. Mechanized spraying of liquid meliorants. IOP: Earth and Environmental Science. 2020. 421(3):032026 (In English). DOI:10.1088/1755-1315/421/3/032026.

9. Vasiliev S.A., Alexeyev V.V., Rechnov A.V. Rapid method for quantitative evaluation of residue on the soil surface. Agrarian Scientific Journal. 2015. N9. 11-13 (In Russian). EDN: UJURLH.

10. Vasiliev S.A. Intelligent technology for quality control of till-age in the precision farming system. Zemledeliye. 2022. N3. 36-41 (In Russian). DOI: 10.24412/0044-3913-2022-3-36-41.

11. Pustovalov R.A., Korotenko T.L. Phenotyping according to morphological and agronomic characteristics of agroecotypes of the world variety of rice in the conditions of the southern region of Russia. Rice Growing. 2022. N2(55). 11-19 (In Russian). DOI: 10.33775/1684-2464-2022-55-2-11-19.

12. Marakaeva, T.V. Phenotypic variability of breeding lines of lentils (Lens culinaris L.) according to elements of seed pro-ductivity in the environmental conditions of the Omsk region. Agrarian Bulletin of the Urals. 2024. Vol. 24. N1. 86-97 (In Russian). DOI: 10.32417/1997-4868-2024-24-01-86-97.

13. Fadeev A.A., Fadeeva M.F., Nikiforova I.I., Ivanova I. Yu. Promising soybean breeding lines of the northern ecotype for developing forage crops. Agrarian Science of the Euro-North-East. 2022. Vol. 23. N2. 203-210 (In Russian). DOI: 10.30766/2072-9081.2022.23.2.203-210.

14. Noskova A.I., Tokranova M.V. Overview of automated monitoring systems. Intellectual Technologies on Transport. 2017. N1. 42-47 (In Russian). EDN: ZIAHDL.

15. Rakutko S.A., Rakutko E.N., Medvedev G.V. Development of an experimental phytotron and its application in the research on the energy-ecological efficiency of indoor plant lighting. Agricultural Machinery and Technologies. 2023. Vol. 17. N2. 40-48 (In Russian). DOI: 10.22314/2073-7599-2023-17-2-40-48.

16. Timofeev E.V., Erk A.E, Razmuk V.A. et al. Survey of modern information systems for monitoring of production processes in agriculture. AgroEcoEngineering. 2021. N1. 4-14 (In Russian). DOI: 10.24411/2713-2641-2021-10274.

17. Lachuga Yu.F., Godzhaev Z.A., Redko I.Ya. Creation and application of mobile multifunctional energy technology complexes. RUDN Journal of Engineering Research. 2022. Vol. 23. N1. 23-29 (In Russian). DOI: 10.22363/2312-8143-2022-23-1-23-29.

18. Kutyrev A.I., Smirnov I.G. A convolutional neural network (Seg-CNN) for recognition, classification, and segmentation of apple fruit branches and stems in images. Horticulture and Viticulture. 2024. N2. 53-62 (In Russian). DOI: 10.31676/0235-2591-2024-2-53-62.

19. Kutyrev A.I., Smirnov I.G. Neural network for apple fruit recognition and classification. Agricultural Scientific Journal. 2023. N8. 123-133 (In Russian). DOI: 10.28983/asj.y2023i8pp123-133.