Applications of Computer Vision Techniques in Viticulture to Assess Canopy Features, Cluster Morphology and Berry Size

Computer vision systems are powerful tools to automate inspection tasks in agriculture. Typical target applications of such systems include grading, quality estimation, yield prediction and monitoring, among others. The capabilities of an artificial vision system go beyond the limited human capacity to evaluate long-term processes objectively and provide valuable data to take decisions that will have great influence in later operations. This work explores the application of machine vision techniques in viticulture from several approaches. The first approach is aimed at working outdoors, developing in-field systems capable of assessing the canopy features of the vineyard (Vitis vinifera L.) by taking digital images and applying computer vision systems. The second approach is aimed at analysing cluster morphology using image analysis. Berry number per cluster and cluster weight were estimated using several algorithms of image processing. Lately, machine vision has been used as a tool to automate the measurement of berry size and weight under laboratory conditions. Manual measurement of the canopy features and yield components are tedious and subjective tasks that can be time-consuming and labour demanding. In this regard, by means of computer vision techniques, a large set of samples can be automatically measured, saving time and providing more objective and precise information. INTRODUCTION Machine vision systems are being used to automate inspection tasks in agriculture and food processing (Cubero et al., 2011; Lorente et al., 2012). Among other characteristics like defect detection or colour estimation, shape and size analysis are features for which image analysis provides an objective and reliable tool. This technology allows automating tasks that can be used in viticulture for different purposes. Canopy features of the fruiting zone are related to fruit microclimate, fruit health status and grape composition (Smart and Robinson, 1991). Image analysis was applied in viticulture for assessing yield (Dunn and Martin, 2004) and the impact of early defoliation (Tardaguila et al., 2010, 2011). Machine vision has many potential applications in viticulture, as a rapid and practical method to estimate canopy features in the field. In viticulture, cluster morphology and berry size are two key parameters, which not only impact the cluster architecture and compactness (leading to looser or tighter clusters), but are also considered as indicators of grape and wine quality (Roby et al., 2004; Matthews and Nuzzo, 2007). However, traditional methods are destructive, labourdemanding, time-consuming and of low accuracy. Computer vision could be also used for assessing cluster morphology and berry weight as a rapid and accurate method. This work presents three approaches of machine vision in viticulture: 1) at canopy level to obtain two key parameters such as leaf area and yield, 2) at a cluster level to estimate berry number per cluster and cluster weight, and 3) to determine berry size and Proc. 1 IW on Vineyard Mechanization & Grape & Wine Quality