A non-linear model for measuring grapevine leaf thickness by means of red-edge/near-infrared spectral reflectance

Vegetation is a key element of our ecology system. The leaf area and its thickness provide valuable information about the status of our environment. Thus, there is a need for accurate, efficient, practical methodologies to estimate this biochemical parameter. Hyperspectral measurement is a means of quickly assessing leaf parameter in situ. In the past decades, there were lots of work (Boyd et al. 6]) that focused on measurement of leaf area index, but very few on measurement of leaf thickness. In this paper, reflectance of grape leaves was measured over the spectral range of 350–1010 nm. The corresponding thickness of leaves from four grapevine cultivars was also measured as part of seventeen field campaigns undertaken during the summer of 2007. An artificial-intelligence technique, the support vector machine (SVM) model, was introduced to establish the relationship between the leaf thickness and red-edge/near-infrared (NIR) reflectance, with variability examined among individual cultivars as well as at various growth stages. The best wavelengths were variable depending on the grape cultivar and growth stage. The SVM model allows compilation of factors such as cultivar and growth stage with spectral information to yield a superior result.