Preview

Agrarian science

Advanced search

Detection of vegetation indices of grape plantations as one of the tools for monitoring the condition of vineyards

https://doi.org/10.32634/0869-8155-2024-383-6-126-131

Abstract

Relevance. Vegetation indices (VI) indices allow one to correlate the estimated signs of growth vigor of a grape plant with productivity values in different periods of phenophases. Grapes respond especially subtly to conditions of weather and climate changes and abnormal weather manifestations. For monitoring vineyards, NDVI is the most effective. The use of Sentinel-2 satellite data in monitoring vineyards has shown high efficiency throughout the entire growing season, and in many countries research is underway on the use of VI to assess the development and productivity of vineyards.

The aim of the work is to find the optimal formula for calculating the productivity of a grape plant based on the values of NDVI.

Methods. Stationary field experience of agrobiological characteristics of grape plantations, processing of digital images of spectral channels of the Sentinel-2 satellite platform. Digital image processing and calculation of NDVI VI were carried out in the GIS SNAP Desktop.

Results. Based on the VI values, the phenological periods of the grape planting were determined to calculate the predicted yield. The presence of a close relationship between vegetation indices, crown density and yield makes it possible to determine the strength of development of grape plants during phenological periods using multispectral satellite images. The developed method for assessing the predicted yield based on the NDVI VI of a grape plant in the phenophases of flowering and growth allows one to calculate the predicted yield with high accuracy in relation to the actual one.

About the Authors

V. A. Orlov
Anapa Zonal Experimental Station of Viticulture and Winemaking – Branch of the Federal State Budget Scientific Institution «North Caucasian Federal Scientific Center of Horticulture, Viticulture, Wine-making»
Russian Federation

Vitaly Aleksandrovich Orlov, Candidate of Agricultural Sciences, Senior Researcher

36 Pionersky Prospekt, Anapa, 353456



A. A. Lukyanov
Anapa Zonal Experimental Station of Viticulture and Winemaking – Branch of the Federal State Budget Scientific Institution «North Caucasian Federal Scientific Center of Horticulture, Viticulture, Wine-making»
Russian Federation

Aleksei Aleksandrovich Lukyanov, Candidate of Agricultural Sciences, Senior Researcher

36 Pionersky Prospekt, Anapa, 353456



References

1. Grishin I.Yu., Timirgaleeva R.R. Methodological aspects of the formation of a system for remote diagnostics of the state of grape agrocenoses in the Crimea. Sevastopol: Branch of Moscow State University in Sevastopol. 2023; 208 (in Russian). ISBN 978-5-907477-77-3. https://doi.org/10.35103/SMSU.2022.14.17.001

2. Rybalko E.A. et al. Management of ground and remote sensing data for remote monitoring of vineyards. Current problems in remote sensing of the Earth from space. 2016; 13(1): 79–92 (in Russian). https://doi.org/10.21046/2070-7401-2016-13-1-79-92

3. Matese A., Di Gennaro S.F. Beyond the traditional NDVI index as a key factor to mainstream the use of UAV in precision viticulture. Scientific Reports. 2021; 11: 2721. https://doi.org/10.1038/s41598-021-81652-3

4. Amirdzhanov A.G. Solar radiation and vineyard productivity. Leningrad: Gidrometeoizdat. 1980; 208 (in Russian).

5. Sams B., Bramley R.G.V., Sanchez L., Dokoozlian N., Ford C., Pagay V. Remote Sensing, Yield, Physical Characteristics, and Fruit Composition Variability in Cabernet Sauvignon Vineyards. American Journal of Enology and Viticulture. 2022; 73(2): 93–105. https://doi.org/10.5344/ajev.2021.21038

6. Junges A.H., Fontana D.C., Lampugnani C.S. Relationship between the normalized difference vegetation index and leaf area in vineyards. Bragantia. 2019; 78(2): 297–305. https://doi.org/10.1590/1678-4499.2018168

7. Devaux N., Crestey T., Leroux C., Tisseyre B. Potential of Sentinel-2 satellite images to monitor vine fields grown at a territorial scale. OENO One. 2019; 53(1): 51–58. https://doi.org/10.20870/oeno-one.2019.53.1.2293

8. Giovos R., Tassopoulos D., Kalivas D., Lougkos N., Priovolou A. Remote Sensing Vegetation Indices in Viticulture: A Critical Review. Agriculture. 2021; 11(5): 457. https://doi.org/10.3390/agriculture11050457

9. Diago M.P., Aquino A., Millan B., Palacios F., Tardaguila J. On-the-go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis. Australian Journal of Grape and Wine Research. 2019; 25(3): 363–374. https://doi.org/10.1111/ajgw.12404

10. Kasimati A. et al. Investigation of the similarities between NDVI maps from different proximal and remote sensing platforms in explaining vineyard variability. Precision Agriculture. 2023; 24(4): 1220–40. https://doi.org/10.1007/s11119-022-09984-2

11. Semenova K.S. Rationale for using the NDVI vegetation index as the main indicator for monitoring the condition of agricultural lands. Proceedings of the International Scientific Conference of Young Scientists and Specialists, dedicated to the 135th anniversary of the birth of A.N. Kostyakov. Moscow: Russian State Agrarian University — Moscow Agricultural Academy named after K.A. Timiryazev. 2022; 1: 44–48 (in Russian). https://www.elibrary.ru/lvyfkh

12. Demisheva E.N. Assessment of the relationship between Normalized difference vegetation index and land surface temperature using remote sensing data. Trudy Povolzhskogo gosudarstvennogo tekhnologicheskogo universiteta. Seriya Tekhnologicheskaya. 2016; (4): 10–16 (in Russian). https://www.elibrary.ru/whwpsz

13. Orlov V.A., Lukyanov A.A. Evaluation of signs of viney land by spectral patterns. Vestnik of Kazan State Agrarian University. 2023; 18(1): 29-37 (in Russian). https://doi.org/10.12737/2073-0462-2023-29-36

14. Zhukov V.D., Scheudgen Z.R. Improving the efficiency of agriculture systems in the Krasnodar region. Scientific Journal of KubSAU. 2019; 151: 104–115 (in Russian). https://doi.org/10.21515/1990-4665-151-010

15. Beibulatov M.R. Modification of the formula for calculating of buds load of grapes bushes. Fruit growing and viticulture of South Russia. 2013; 24: 68–74 (in Russian). https://www.elibrary.ru/rkofnp

16. Guseynov Sh.N., Chigrik B.V., Gordeeva N.G. Resources of increase of genetic potential at age-old classical grades of grapes. Fruit growing and viticulture of South Russia. 2009; 1: 14–22 (in Russian). https://www.elibrary.ru/mzjhuv


Review

For citations:


Orlov V.A., Lukyanov A.A. Detection of vegetation indices of grape plantations as one of the tools for monitoring the condition of vineyards. Agrarian science. 2024;(6):126-131. (In Russ.) https://doi.org/10.32634/0869-8155-2024-383-6-126-131

Views: 191


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0869-8155 (Print)
ISSN 2686-701X (Online)
X