Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation

Abstract

Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r2 = 0.950), which outperformed RGB imagery (r2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

“…They estimated the berry number and berry size parameter from the partial point clouds. Hacking et al [30] used a Kinect 3D sensor to reconstruct 20 grape bunches from the field. They used fruit volume estimation to predict yield with r = 0.61.…”

Editorial: Special Issue “Emerging Sensor Technology in Agriculture”

“…The authors highlighted the potential applicability of this method to determine the spatial variability of cluster compactness in commercial vineyards. As was stated by Palacios et al [5] and Hacking et al [4], fruit detection is the first mandatory step to perform other calculations. In this regard, Zemmour et al [2], presented an automatic parameter-tuning procedure for fruit detection.…”

“…The agricultural industry has been greatly affected by climate change; therefore, to be successful in overcoming these effects and remain competitive and sustainable in the market, there is the need to support research and application development of new and emerging sensor technologies and their applications in agriculture. A total of 13 papers were published in this Special Issue entitled: "Emerging Sensor Technology in Agriculture", and the topics addressed include different emerging technologies with applications on ecosystems (grasslands) [1] and several agriculture crops such as peppers [2], apples [2,3], grapevines [2,[4][5][6][7], cocoa trees [6], citrus [8], legumes [9], wheat and rice [10,11]. Two papers were also related to the use of remote sensing to detect forage quality [9], regions of interest of pigs [12], and pesticide droplet deposition [13] using machine learning.…”

“…From a comparison point of view, the authors took into consideration some basic economic and practical aspects of the methodologies. Hacking et al [4] used a similar approach to determine yield in grapevines. Another UAV-based study investigated the effect of eddies formed at low altitude in wheat to estimate water status effectively and other physiological parameters in rice [11].…”

“…Yield estimation is a key topic in agriculture in general, and it is very relevant in viticulture since winegrowers need such information to manage several logistic aspects at the cellars. In Hacking et al [4], 2D (RGB images) and 3D (RGB-D) approaches were tested and compared, providing promising results and perspectives in terms of the potential application of these technologies at the vineyard scale (in situ yield estimation). Another interesting use in viticulture was presented in the research of Palacios et al [5], where they combined computer vision (RGB images) and machine learning for assessing cluster compactness (degree of the aggregation of its berries) under field conditions (system mounted on an all-terrain vehicle).…”