Ground-Based Thermal Imaging for Assessing Crop Water Status in Grapevines over a Growing Season

• Published in: Agronomy (Basel) 2022-02, Vol.12 (2), p.322
• Main Authors: Zhou, Zheng, Diverres, Geraldine, Kang, Chenchen, Thapa, Sushma, Karkee, Manoj, Zhang, Qin, Keller, Markus
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Cameras; Canopies; canopy temperature; Climate change; Color imagery; Crops; CWSI; Decision support systems; Digital cameras; Digital imaging; Experimental farms; Farms; Grapevines; Growing season; Heat detection; Image acquisition; Irrigation; Irrigation systems; Irrigation water; leaf water potential; Leaves; precision viticulture; Robustness; Sensors; Thermal imaging; Unmanned aerial vehicles; Vegetation; Vineyards; Water management; Water potential; Water stress; Wineries & vineyards; Wines
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12020322

The quality of wine grapes in dry climates greatly depends on utilizing optimal amounts of irrigation water during the growing season. Robust and accurate techniques are essential for assessing crop water status in grapevines so that both over-irrigation and excessive water deficits can be avoided. This study proposes a robust strategy to assess crop water status in grapevines. Experiments were performed on Riesling grapevines (Vitis vinfera L.) planted in rows oriented north–south and subjected to three irrigation regimes in a vineyard maintained at an experimental farm in southeastern Washington, USA. Thermal and red–green–blue (RGB) images were acquired during the growing season, using a thermal imaging sensor and digital camera installed on a ground-based platform such that both cameras were oriented orthogonally to the crop canopy. A custom-developed algorithm was created to automatically derive canopy temperature (Tc) and calculate crop water stress index (CWSI) from the acquired thermal-RGB images. The relationship between leaf water potential (Ψleaf) and CWSI was investigated. The results revealed that the proposed algorithm combining thermal and RGB images to determine CWSI can be used for assessing crop water status of grapevines. There was a correlation between CWSI and Ψleaf with an R-squared value of 0.67 for the measurements in the growing season. It was also found that CWSI from the shaded (east) side of the canopy achieved a better correlation with Ψleaf compared to that from the sunlit (west) side around solar noon. The created algorithm allowed real-time assessment of crop water status in commercial vineyards and may be used in decision support systems for grapevine irrigation management.