Grapevine yield prediction using image analysis - improving the estimation of non-visible bunches

Yield forecast is an issue of utmost importance for the entire grape and wine sectors. There are several methods for vineyard yield estimation. The ones based on estimating yield components are the most commonly used in commercial vineyards. Those methods are generally destructive and very labor intensive and can provide inaccurate results as they are based on the assessment of a small sample of bunches. Recently, several attempts have been made to apply image analysis technologies for bunch and/or berries recognition in digital images. Nonetheless, the effectiveness of image analysis in predicting yield is strongly dependent of grape bunch visibility, which is dependent on canopy density at fruiting zone and on bunch number, density and dimensions. In this work data on bunch occlusion obtained in a field experiment is presented. This work is set-up in the frame of a research project aimed at the development of an unmanned ground vehicle to scout vineyards for non-intrusive estimation of canopy features and grape yield. The objective is to evaluate the use of explanatory variables to estimate the fraction of non-visible bunches (bunches occluded by leaves). In the future, this estimation can potentially improve the accuracy of a computer vision algorithm used by the robot to estimate total yield. In two vineyard plots with Encruzado (white) and Syrah (red) varieties, several canopy segments of 1 meter length were photographed with a RGB camera and a blue background, close to harvest date. Out of these images, canopy gaps (porosity) and bunches’ region of interest (ROI) files were computed in order to estimate the corresponding projected area. Vines were then defoliated at fruiting zone, in two steps and new images were obtained before each step. Overall the area of bunches occluded by leaves achieved mean values between 67% and 73%, with Syrah presenting the larger variation. A polynomial regression was fitted between canopy porosity (independent variable) and percentage of bunches not occluded by leaves which showed significant R2 values of 0.83 and 0.82 for the Encruzado and Syrah varieties, respectively. Our results show that the fraction of non-visible bunches can be estimated indirectly using canopy porosity as explanatory variable, a trait that can be automatically obtained in the future using a laser range finder deployed on the mobile platforminfo:eu-repo/semantics/publishedVersio

Vineyard yield estimation using image analysis – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation is one of the main goals of the wine industry, this because with an accurate yield estimation it is possible to have a significant reduction in production costs and a better management of the wine industry. Traditional methods for yield estimation are laborious and time consuming, for these reasons in the last years we are witnessing to the development of new methodologies, most of which are based on image analysis. Thanks to the continuous updating and improvement of the computer vision techniques and of the robotic platforms, image analysis applied to the yield estimation is becoming more and more efficient. In fact the results shown by the different studies are very satisfying, at least as regards the estimation of what is possible to see, while are under development several procedures which have the objective to estimate what is not possible to see, due to bunch occlusion by leaves and by others clusters. I this work the different methodologies and the different approaches used for yield estimation are described, including both traditional methods and new approaches based on image analysis, in order to present the advantages and disadvantages of each of themN/