Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes

• Published in: Agriculture (Basel) Vol. 8; no. 7; p. 94
• Main Authors: Anastasiou, Evangelos, Balafoutis, Athanasios, Darra, Nikoleta, Psiroukis, Vasileios, Biniari, Aikaterini, Xanthopoulos, George, Fountas, Spyros
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2018
• Subjects: Agricultural production; Agriculture; Correlation analysis; Crop yield; crop yield and quality estimation; Crops; Cultivation; Deformation; Detection; Fruits; Grapes; Growth models; Irrigation; Landsat; Landsat satellites; Nondestructive testing; Normalized difference vegetative index; precision viticulture; Quality; Quality assessment; Remote sensing; Satellite imagery; Satellite sensing; Satellites; Sensors; spectral vegetation index; table grapes; Vegetation; Vegetation index; Vitaceae; Wineries & vineyards; Greece
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture8070094

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.