Vineyard yeld estimation by VINBOT robot - preliminary results with the white variety Viosinho - Lopes, C.M. (2017)
Nowadays it is recognized that vineyard yield estimation can bring several benefits to all the vine and wine industry and, consequently, there is a strong demand for fast and reliable yield estimation methods. Recently a strong effort has been made on developing machine vision tools to automatically estimate vineyard yields evolving several research teams worldwide. In this paper we aim to present preliminary results obtained in the frame of an European research project (VINBOT: “Autonomous cloud-computing vineyard robot to optimise yield management and wine quality”) focus on yield estimation. A ground truth evaluation trial was set up in an experimental vineyard with the white variety Viosinho, trained on a vertical shoot positioning system and spur pruned. A sample of contiguous vines was labeled and submitted to a detailed assessment of vegetative and reproductive data to feed a viticulture data library. The vines were scanned during the ripening period of the 2015 season by the VINBOT sensor head composed with a set of sensors capable of capturing vineyard images and 3D data. Ground truth data was used to relate with images taken by the sensors and to test algorithms of image analysis. In this paper we present and discuss the relationships between actual and estimated yield computed using the surface occupied by the grape clusters in the images. Our preliminary results showed that, despite of a slight underestimation of the ground truth, caused mainly by cluster occlusion, when the canopy density allows visualization of most part of the clusters, the yield can be estimated by machine vision with a high fidelity. Further research is ongoing to test those devices and methodologies in other varieties and to improve the estimation accuracy