Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes
by
Evangelos Anastasiou 1,*, Athanasios Balafoutis 1,2, Nikoleta Darra 1, Vasileios Psiroukis 1, Aikaterini Biniari 3, George Xanthopoulos 1 and Spyros Fountas 1
Evangelos Anastasiou 1,*, Athanasios Balafoutis 1,2, Nikoleta Darra 1, Vasileios Psiroukis 1, Aikaterini Biniari 3, George Xanthopoulos 1 and Spyros Fountas 1
1
Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
2
Institute for Bio-Economy & Agri-Technology, Centre of Research & Technology Hellas, Dimarchou Georgiadou 118, 38221 Volos, Greece
3
Faculty of Crop Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
*
Author to whom correspondence should be addressed.
Agriculture 2018, 8(7), 94; https://doi.org/10.3390/agriculture8070094
Received: 30 April 2018 / Revised: 21 June 2018 / Accepted: 22 June 2018 / Published: 26 June 2018
(This article belongs to the Special Issue Precision Agriculture)
Table grapes are a crop with high nutritional value that need to be monitored often to achieve high yield and quality. Non-destructive methods, such as satellite and proximal sensing, are widely used to estimate crop yield and quality characteristics, and spectral vegetation indices (SVIs) are commonly used to present site specific information. The aim of this study was the assessment of SVIs derived from satellite and proximal sensing at different growth stages of table grapes from veraison to harvest. The study took place in a commercial table grape vineyard (Vitis vinifera cv. Thompson Seedless) during three successive cultivation years (2015–2017). The Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were calculated by employing satellite imagery (Landsat 8) and proximal sensing (Crop Circle ACS 470) to assess the yield and quality characteristics of table grapes. The SVIs exhibited different degrees of correlations with different measurement dates and sensing methods. Satellite-based GNDVI at harvest presented higher correlations with crop quality characteristics (r = 0.522 for berry diameter, r = 0.537 for pH, r = 0.629 for berry deformation) compared with NDVI. Proximal-based GNDVI at the middle of veraison presented higher correlations compared with NDVI (r = −0.682 for berry diameter, r = −0.565 for berry deformation). Proximal sensing proved to be more accurate in terms of table grape yield and quality characteristics compared to satellite sensing.
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Keywords:
spectral vegetation index; precision viticulture; remote sensing; table grapes; crop yield and quality estimation
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MDPI and ACS Style
Anastasiou, E.; Balafoutis, A.; Darra, N.; Psiroukis, V.; Biniari, A.; Xanthopoulos, G.; Fountas, S. Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes. Agriculture 2018, 8, 94.
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