Development and validation of a temperature measurement correction algorithm under intensive solar radiation conditions
https://doi.org/10.32634/0869-8155-2025-400-11-144-158
Abstract
Relevance. Accurate air temperature measurement is a fundamental task in vineyard microclimate monitoring, as this parameter directly affects phenological processes, pathogen development, and the formation of grape quality characteristics. Available meteorological stations possess a significant limitation — substantial systematic errors when measuring temperature under conditions of intense solar radiation. The development of methods to minimize these errors is critically important for advancing precision viticulture technologies.
Methods. The study is based on theoretical analysis and experimental validation of thermophysical processes in radiation shields. A specialized measurement setup was developed with meteorological systems: a ventilated shield with photovoltaic power supply and a non-ventilated shield, equipped with wind speed and solar radiation sensors. An original physical model of heat balance was proposed, accounting for convective heat exchange, radiative effects, and geometric characteristics of protective devices. An adaptive iterative algorithm was developed for real-time temperature correction calculations on microcontroller systems with mechanisms for handling singular states and ensuring stable convergence.
Results. It was experimentally confirmed that non-ventilated shields demonstrate systematic errors up to 3 °C under high solar radiation. Ventilated shields reduce maximum deviations to 1 °C but require regular maintenance. The developed mathematical correction algorithm outperforms both alternative solutions: maximum deviations do not exceed 0.6 °C, mean deviations are 0.2 °C, normalized root-mean-square error is 3.5%, and the Kling — Gupta criterion reaches 0.993. The proposed solution provides an optimal balance of accuracy, costeffectiveness, and reliability for creating affordable dense networks for vineyard microclimate monitoring.
Keywords
About the Authors
P. N. KuznetsovRussian Federation
Pavel Nikolaevich Kuznetsov, Candidate of Technical Sciences, Leading Researcher; Candidate of Technical Sciences, Associate Professor
V. P. Evstigneev
Russian Federation
Vladislav Pavlovich Evstigneev, Candidate of Physical and Mathematical Sciences, Аssociate Рrofessor
31 Kirova Str., Yalta, 298600
33 Universitetskaya Str., Sevastopol, 299053
D. Yu. Kotelnikov
Russian Federation
Dmitry Yurievich Kotelnikov, Junior Research Assistant; Associate Professor
31 Kirova Str., Yalta, 298600
33 Universitetskaya Str., Sevastopol, 299053
D. Yu. Voronin
Russian Federation
Dmitry Yurievich Voronin, Candidate of Technical Sciences, Аssociate Рrofessor
31 Kirova Str., Yalta, 298600
References
1. . Jones G.V., Davis R.E. Using a synoptic climatological approach to understand climate–viticulture relationships. International Journal of Climatology. 2000; 20(8): 813–837. https://doi.org/10.1002/1097-0088(20000630)20:8<813::AIDJOC495>3.0.CO;2-W
2. Korosi G.A., Mee P.T., Powell K.S. Influence of temperature and humidity on mortality of grapevine phylloxera Daktulosphaira vitifoliae clonal lineages: a scientific validation of a disinfestation procedure for viticultural machinery. Australian Journal of Grape and Wine Research. 2012; 18(1): 43–47. https://doi.org/10.1111/j.1755-0238.2011.00168.x
3. Matese A. et al. Spatial variability of meteorological conditions at different scales in viticulture. Agricultural and Forest Meteorology. 2014; 189–190: 159–167. https://doi.org/10.1016/j.agrformet.2014.01.020
4. Ammoniaci M., Kartsiotis S.-P., Perria R., Storchi P. State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture. Agriculture. 2021; 11(3): 201. https://doi.org/10.3390/agriculture11030201
5. Karimi N., Arabhosseini A., Karimi M., Kianmehr M.H. Web-based monitoring system using Wireless Sensor Networks for traditional vineyards and grape drying buildings. Computers and Electronics in Agriculture. 2018; 144: 269–283. https://doi.org/10.1016/j.compag.2017.12.018
6. Bramley R.G.V. Precision Viticulture: Managing vineyard variability for improved quality outcomes. Reynolds A.G. (ed.). Managing Wine Quality. 2nd Edition. Woodhead Publishing. 2021; 1: 541–586. https://doi.org/10.1016/B978-0-08-102067-8.00002-6
7. Romero P., Navarro J.M., Ordaz P.B. Towards a sustainable viticulture: The combination of deficit irrigation strategies and agroecological practices in Mediterranean vineyards. A review and update. Agricultural Water Management. 2022; 259: 107216. https://doi.org/10.1016/j.agwat.2021.107216
8. Darouich H. et al. Water Use and Soil Water Balance of Mediterranean Vineyards under Rainfed and Drip Irrigation Management: Evapotranspiration Partition and Soil Management Modelling for Resource Conservation. Water. 2022; 14(4): 554. https://doi.org/10.3390/w14040554
9. Cortiñas Rodríguez J.A., González-Fernández E., Fernández-González M., Vázquez-Ruiz R.A., Aira M.J. Fungal Diseases in Two North-West Spain Vineyards: Relationship with Meteorological Conditions and Predictive Aerobiological Model. Agronomy. 2020; 10(2): 219. https://doi.org/10.3390/agronomy10020219
10. Kuznetsov P.N., Kotelnikov D.Y., Shchekin V.Y., Koltsov A.D., Kabankova E.N. Intelligent complex of monitoring and diagnostics of grape plantations. IOP Conference Series: Earth and Environmental Science. 2022; 981: 032020. https://doi.org/10.1088/1755-1315/981/3/032020
11. Kuznetsov P.N., Kotelnikov D.Yu., Voronin D.Yu. Technology of automated monitoring of the vineyard condition. Agrarian science. 2023; (3): 109–116 (in Russian). https://doi.org/10.32634/0869-8155-2023-368-3-109-116
12. Kuznetsov P.N., Kotelnikov D.Yu. Automated technological complex for monitoring and diagnostic vineyard. Don agrarian science bulletin. 2021; (4): 16–23 (in Russian). https://elibrary.ru/auqdkc
13. Onesti G., González-Domínguez E., Rossi V. Accurate prediction of black rot epidemics in vineyards using a weather-driven disease model. Pest Management Science. 2016; 72(12): 2321–2329. https://doi.org/10.1002/ps.4277
14. Sadras V.O., Petrie P.R. Predicting the time course of grape ripening. Australian Journal of Grape and Wine Research. 2012; 18(1): 48–56. https://doi.org/10.1111/j.1755-0238.2011.00169.x
15. Sirsat M.S., Mendes-Moreira J., Ferreira C., Cunha M. Machine Learning predictive model of grapevine yield based on agroclimatic patterns. Engineering in Agriculture, Environment and Food. 2019; 12(4): 443–450. https://doi.org/10.1016/j.eaef.2019.07.003
16. Ohana-Levi N., Munitz S., Netzer Y. Grapevine stem water potential seasonal curves: response to meteorological conditions, and association to yield and red wine quality. Agricultural and Forest Meteorology. 2023; 342: 109755. https://doi.org/10.1016/j.agrformet.2023.109755
17. Treder W., Klamkowski K., Tryngiel-Gać A., Wójcik K. Evaluating the suitability of a new telemetric capacitance-based measurement system for real-time application in irrigation and fertilization management. Journal of Water and Land Development. 2023; 56(1–3): 67–73. https://doi.org/10.24425/jwld.2023.143746
18. Ferrández-Pastor F.J., García-Chamizo J.M., Nieto-Hidalgo M., Mora-Martínez J. Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors. 2018; 18(6): 1731. https://doi.org/10.3390/s18061731
19. Ioannou K., Karampatzakis D., Amanatidis P., Aggelopoulos V., Karmiris I. Low-Cost Automatic Weather Stations in the Internet of Things. Information. 2021; 12(4): 146. https://doi.org/10.3390/info12040146
20. da Cunha A.R. Evaluation of measurement errors of temperature and relative humidity from HOBO data logger under different conditions of exposure to solar radiation. Environmental Monitoring and Assessment. 2015; 187(5): 236. https://doi.org/10.1007/s10661-015-4458-x
21. Sun X., Yan S., Wang B., Xia L., Liu Q., Zhang H. Air Temperature Error Correction Based on Solar Radiation in an Economical Meteorological Wireless Sensor Network. Sensors. 2015; 15(8): 18114–18139. https://doi.org/10.3390/s150818114
22. Erell E., Leal V., Maldonado E. Measurement of air temperature in the presence of a large radiant flux: an assessment of passively ventilated thermometer screens. Boundary-Layer Meteorology. 2005; 114(1): 205–231. https://doi.org/10.1007/s10546-004-8946-8
23. Hubbard K.G., Lin X. Realtime data filtering models for air temperature measurements. Geophysical Research Letters. 2002; 29(10): 67-1–67-4. https://doi.org/10.1029/2001GL013191
24. Hubbart J.A. An Inexpensive Alternative Solar Radiation Shield for Ambient Air Temperature Micro-Sensors. Journal of Natural & Environmental Sciences. 2011; 2(2): 9–14.
25. Tarara J.M., Hoheisel G.-A. Low-cost Shielding to Minimize Radiation Errors of Temperature Sensors in the Field. HortScience. 2007; 42(6): 1372–1379. https://doi.org/10.21273/HORTSCI.42.6.1372
26. Yang J., Deng X., Liu Q., Ding R. Design and experimental study of an effective, low-cost, naturally ventilated radiation shield for monitoring surface air temperature. Meteorology and Atmospheric Physics. 2021; 133(2): 349–357. https://doi.org/10.1007/s00703-020-00754-1
27. Richardson S.J., Brock F.V., Semmer S.R., Jirak C. Minimizing Errors Associated with Multiplate Radiation Shields. Journal of Atmospheric and Oceanic Technology. 1999; 16(11): 1862–1872. https://doi.org/10.1175/1520-0426(1999)016<1862:MEAWMR>2.0 .CO;2
28. Yang S.-H., Lee C.-G., Kim J.-Y., Lee W.-K., Ashtinai-Araghi A., Rhee J.-Y. Effects of Fan-Aspirated Radiation Shield for Temperature Measurement in Greenhouse Environment. Journal of Biosystems Engineering. 2012; 37(4): 245–251. https://doi.org/10.5307/JBE.2012.37.4.245
29. Yang J., Liu Q., Dai W., Ding R. A temperature error correction method for a naturally ventilated radiation shield. Journal of Atmospheric and Solar-Terrestrial Physics. 2016; 149: 40–45. https://doi.org/10.1016/j.jastp.2016.09.010
30. Georges C., Kaser G. Ventilated and unventilated air temperature measurements for glacier-climate studies on a tropical high mountain site. Journal of Geophysical Research: Atmospheres. 2002; 107(D24): ACL15-1–ACL15-10. https://doi.org/10.1029/2002JD002503
31. Jenkins G. A comparison between two types of widely used weather stations. Weather. 2014; 69(4): 105–110. https://doi.org/10.1002/wea.2158
32. Dai W., Tan M., Zhu H. Design of a radiation shield applied to surface air temperature monitoring. Journal of Instrumentation. 2023; 18: P02015. https://doi.org/10.1088/1748-0221/18/02/P02015
33. Yang J., Liu Q., Dai W. A method for solar radiation error correction of temperature measured in a reinforced plastic screen for climatic data collection. International Journal of Climatology. 2018; 38(3): 1328–1336. https://doi.org/10.1002/joc.5247
34. Matese A., Di Gennaro S.F., Zaldei A., Genesio L., Vaccari F.P. A wireless sensor network for precision viticulture: The NAV system. Computers and Electronics in Agriculture. 2009; 69(1): 51–58. https://doi.org/10.1016/j.compag.2009.06.016
35. Morais R., Fernandes M.A., Matos S.G., Serôdio C., Ferreira P.J.S.G., Reis M.J.C.S. A ZigBee multi-powered wireless acquisition device for remote sensing applications in precision viticulture. Computers and Electronics in Agriculture. 2008; 62(2): 94–106. https://doi.org/10.1016/j.compag.2007.12.004
36. Hubbard K.G., Lin X., Walter-Shea E.A. The Effectiveness of the ASOS, MMTS, Gill, and CRS Air Temperature Radiation Shields. Journal of Atmospheric and Oceanic Technology. 2001; 18(6): 851–864. https://doi.org/10.1175/1520-0426(2001)018<0851:TEOTAM>2.0.
37. CO;2
38. Incropera F.P., DeWitt D.P. Fundamentals of Heat and Mass Transfer. 4th Edition. New York: Wiley. 1996; xxiii, 886. ISBN 0471304603
39. Agarwal, Divya, Sneh J. Devra, Tiwari A. Probability and probability distribution as decision making tool in agriculture: A review. International Journal of Statistics and Applied Mathematics. 2024; 9(5): 208–213
40. Kobzar A.I. Applied Mathematical Statistics. For Engineers and Researchers. 2nd Edition. Moscow: Fizmatlit. 2012; 816 (in Russian).
41. Moriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harmel R.D., Veith T.L. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE. 2007; 50(3): 885–900. https://doi.org/10.13031/2013.23153
42. Gupta H.V., Kling H., Yilmaz K.K., Martinez G.F. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology. 2009; 377(1–2): 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
43. Rausand M., Barros A., Hoyland A. System Reliability Theory: Models, Statistical Methods, and Applications. 3rd Edition. Wiley. 2020; 864. ISBN 978-1-119-37352-0
Review
For citations:
Kuznetsov P.N., Evstigneev V.P., Kotelnikov D.Yu., Voronin D.Yu. Development and validation of a temperature measurement correction algorithm under intensive solar radiation conditions. Agrarian science. 2025;(11):144-158. (In Russ.) https://doi.org/10.32634/0869-8155-2025-400-11-144-158



































