Grapevine bunch volume estimation using an infrared depth sensor under laboratory and field conditions

An accurate yield estimation is a constant demand in the winemaking industry. Current methods are destructive and time-consuming. In general, yield prediction is performed using weather and historical yield data combined with manual samplings in the field. The aim of this study is to define a novel method to estimate bunch volume with a non-destructive technique. In this study 3D images of 21 grapevine bunches (Vitis vinifera L. cv. Shiraz) were obtained by a Kinect infrared depth sensor using three different approaches: i) laboratory condition (standard light environment); ii) field condition full canopy, prior to leaf removal and iii) field condition after leaf removal (100% of leaf removal in the bunch zone). The bunch volume obtained by the traditional water displacement method was used as a reference value. Results show a high correlation between the reference volume and the estimated volume obtained by 3D images in laboratory condition with a coefficient of determination (r2) of 0.93. In field conditions, the leaf removal treatment was the best result for estimating the bunch volume with an r2 value of 0.82. Therefore, it is possible to ensure that the use of aninfrared depth sensor is a valid method to estimate bunch volume in both laboratory and field condition. This novel approach could be the first step in a vineyard yield estimation program if an elaborate sampling method is provided.