Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards

Abstract

Yield prediction is crucial for the management of harvest and scheduling wine production operations. Traditional yield prediction methods rely on manual sampling and are time-consuming, making it difficult to handle the intrinsic spatial variability of vineyards. There have been significant advances in automatic yield estimation in vineyards from on-ground imagery, but terrestrial platforms have some limitations since they can cause soil compaction and have problems on sloping and ploughed land. The analysis of photogrammetric point clouds generated with unmanned aerial vehicles (UAV) imagery has shown its potential in the characterization of woody crops, and the point color analysis has been used for the detection of flowers in almond trees. For these reasons, the main objective of this work was to develop an unsupervised and automated workflow for detection of grape clusters in red grapevine varieties using UAV photogrammetric point clouds and color indices. As leaf occlusion is recognized as a major challenge in fruit detection, the influence of partial leaf removal in the accuracy of the workflow was assessed. UAV flights were performed over two commercial vineyards with different grape varieties in 2019 and 2020, and the photogrammetric point clouds generated from these flights were analyzed using an automatic and unsupervised algorithm developed using free software. The proposed methodology achieved R2 values higher than 0.75 between the harvest weight and the projected area of the points classified as grapes in vines when partial two-sided removal treatment, and an R2 of 0.82 was achieved in one of the datasets for vines with untouched full canopy. The accuracy achieved in grape detection opens the door to yield prediction in red grape vineyards. This would allow the creation of yield estimation maps that will ease the implementation of precision viticulture practices. To the authors’ knowledge, this is the first time that UAV photogrammetric point clouds have been used for grape clusters detection.

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

“…As SfM image orientation is based on automatic calibration, the procedure accommodates conventional non-metric cameras lacking calibration certificates, such as used in other studies to develop three-dimensional crop models [25]. Research deploying three-dimensional modelling has successfully gathered information on production quality variables such as harvest weight to estimate yield, leaf area index (LAI) for information on vineyard environmental conditions, and vine volume to improve crop management and reduce inputs [26][27][28].…”

Computer Vision and Deep Learning for Precision Viticulture

“…This technique is appropriate for studies in laboratories or in the field with a vehicle designed for phenotyping [16]. It can also be employed in natural conditions in more straightforward situations, such as the detection of red grapes from UAV images after defoliation [9,68]. However, the grapes must have attained the maturity to discriminate their color from the ground or the leaves.…”

“…Not suitable for daylight processing in natural condition [71] Pixel classification [68,[73][74][75][76][77][79][80][81][82][83]85,89,115].…”

Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review

“…The images can be acquired using a still camera [4,10,96] in a laboratory or under field conditions, and also by other optical or multispectral proximal sensors, on-the-go using ATVs [12,40,97], other terrestrial autonomous vehicles [7,48] including autonomous robot systems [15,102,106], UAVs [101,105] that cope with the limitations of ground vehicles regarding field conditions (slopes and soil) or in a more simple way on foot with a smartphone [57].…”