Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

Abstract

In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R2 was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R2 = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight.

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

“…The estimation of leaf area allows modulating the vegetative-productive balance, together with other parameters such as, for example, the weight of pruning wood closely related to the variability of vine vigour. Some studies developed on the basis of the SfM method have provided interesting correlation results between vine canopy volume and pruning weight (R 2 range 0.56-0.71) [18]. The 3D models provide a detailed view of the vineyard rows and therefore these results can be used as input for autonomous driving of unmanned ground vehicles [227,228], and can provide useful information to carry out operations such as pruning in an automated form by bud detection [43].…”

“…For soil monitoring, data are collected on electrical conductivity [12], soil structure and soil moisture [13]. Equally important are measurements based on the plants themselves, as it is possible to survey canopy vigour and development, LAI [14][15][16][17][18] and grape yield and quality [19,20]. These canopy surveys are carried out using autonomous and semi-autonomous devices such as unmanned aerial vehicles (UAV) or unmanned ground vehicles (UGV).…”

VineLiDAR: High-resolution UAV-LiDAR vineyard dataset acquired over two years in northern Spain

“…There are already studies showing that UAV-based LiDAR can effectively monitor crop changes [2] and provide efficient tracking of biomass and nitrogen uptake [3] . Additionally, the use of 3D point cloud applications in vineyards has been demonstrated to effectively estimate pruning weight [4] , detect vineyards, evaluate vine-rows features [5] , and generate accurate digital surface models (DSMs) that aid in creating digital terrain models (DTMs) and Canopy Height Models (CHM) for canopy management [6] and disease detection [7] . On the other hand, there is a growing interest in estimating vineyard parameters from satellite imagery like Sentinel 2 [8 , 9] .…”

Accuracy Evaluation and Branch Detection Method of 3D Modeling Using Backpack 3D Lidar SLAM and UAV-SfM for Peach Trees during the Pruning Period in Winter

“…For the image-based method, it was confirmed that the detection of tree height and diameter at breast height had high accuracy, but there is a lack of discussion on branch distribution, extraction methods for small branches, and comparison of the tree potential for agricultural applications related to fruit trees [37][38][39][40][41]. There are few discussions on the effect of the 3D reconstruction of fruit trees or the effect on the model stability during winter pruning periods [42]. On the other hand, there has not been enough discussion on whether the 3D lidar SLAM method is better than the UAV-SfM method, which has been used widely in recent years concerning modeling, detection accuracy, and computation time [15,16,[42][43][44].…”

“…There are few discussions on the effect of the 3D reconstruction of fruit trees or the effect on the model stability during winter pruning periods [42]. On the other hand, there has not been enough discussion on whether the 3D lidar SLAM method is better than the UAV-SfM method, which has been used widely in recent years concerning modeling, detection accuracy, and computation time [15,16,[42][43][44].…”