Proximal Radar Sensors for Precision Viticulture

Abstract

In this paper, we report the accurate estimation of vine grape yield from a three-dimensional radar imagery technique. Three ground-based frequency-modulated continuous-wave radars operating respectively at 24GHz, 77GHz and 122GHz are used for the contact-less estimation of grape mass in vineyards. 3D radar images are built from the beam-scanning of the vine plants and allow estimating the mass of grapes from the computation of appropriate statistical estimators. These estimators are derived from the measured polarization and magnitude of radar echoes. It is shown that the estimation of grape mass from the proposed ground-based radar imagery technique at millimeter-wave frequency range may be accurate within 1 per cent.

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

“…The standard or traditional methods retrieve limited data and produce a static prediction in a multi-step process of determining average number of clusters per vine, number of berries per cluster, and weight per cluster or berry with the growth overall 10% error greatly dependent on adequate staffing and extensive historical databases of cluster weights and yields [37] Computer vision and image processing are leading the alternative methods and are one of the most utilized techniques for attempting an early yield estimation. Still, different approaches such as Synthetic Aperture Radar (SAR), low frequency ultrasound [38], RF Signals [39], counting number of flowers [40][41][42][43][44][45][46][47], Boolean model application [48], shoot count [49], shoot biomass [50,51], frequency-modulated continuous-wave (FMCW) radar [52,53], detection of specular spherical reflection peaks [54], the combination of RGB and multispectral imagery [55] along with derived occlusion ratios, are alternative methods.…”

“…One major disadvantage is that 2-D or even stereo images do not bring measurement data in the depth of the scene [53], and image analysis algorithms are very dependent on occlusion [98,99], which can constitute self-occlusions: berries hidden behind berries within the same grape cluster, cluster-occlusions: berries hidden behind other grape clusters, and vine-occlusions: berries hidden behind the leaves and shoots of the vine [7]. Furthermore, environmental dynamics such as leaf movements due to wind and changing illumination conditions are challenging when working under field conditions [108].…”

“…Threedimensional radar imagery techniques for yield determination are reported here as an alternative to remote estimations based on proximal optical or multispectral proximal or remote sensors, to deal with limitations regarding performance, occlusion, and light issues in field conditions. Henry et al [52,53] used ground-based frequency-modulated continuous-wave radars operating at 24, 77, and 122 GHz to estimate grape mass without contact. The major advantage is that most grapes can be detected under field conditions even if leaves, shoots, or other grapes partially or fully hide them.…”

Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards

“…Bramley et al [32], Taskos et al [33], Anastasiou et al [29], Reynolds et al [78], Primicerio et al [79], Magarreiro et al [45], Borgogno-Mondino et al [46], and Stamatiadis et al [31] utilized VIs or VBVs obtained from Crop Circle and remotely sensed imagery for assessing vineyard's conditions and their relation to yield variability. Henry et al [80], Arnó et al [6], and Stamatiadis et al [81] used several different proximal sensors, along with GPS and VRA to evaluate the characteristics of vineyards, while other scientists, such as Meggio et al [19], Xue and Su [42], and Martínez-Beltrán et al [82], utilized for the same reason various remote sensors (hyperspectral or thermal).…”

Classification of Radar Echoes for Identification and Remote Reading of Chipless Millimeter-Wave Sensors

“…The following parameters are used to compute the isolines: initial echo level is -80dB, the maximal number of local maxima per isolines is 1, and the area inside an isoline ranges from 3 px 2 to 20 px 2 . A more detailed description of the algorithm can be found in [20].The segmentation algorithm is not applied to the two copolarization configurations p=V V and HH because the lower SNR in these configurations may impact the segmentation efficiency ; (v) Isolines are indexed with some of their features, such as their coordinates, shape or echo levels inside their boundaries. We denote by C p i the i th isoline in polarization configuration p. As it will be justified in Section IV-D, only cross-polarization configurations p=V H and HV are considered in this step ; (vi) To identify the radar echoes from the pressure sensors, only isolines C V H i and C HV j that intersect in the plane (θ k ,ϕ k ) are selected.…”