This paper presents a method for the remote interrogation of batteryless humidity sensors based on statistical estimations and 3-D beam scanning techniques. As a proof-of-concept demonstration, a Van-Atta reflectarray sensor is interrogated with a 24-GHz frequency-modulated continuous-wave transceiver at various reading ranges. The extension of the proposed approach to passive or moving sensors is reported, and the measurement results are discussed. The relative humidity (RH) is derived from the statistical analysis of multidimensional radar echoes, while method is proposed to detect the sensor among clutter without knowing its exact position. Moreover, it is demonstrated that the cross-polarization effect of the Van-Atta reflectarray combined with the 3-D beam scanning technique allows the indoor measurement of the RH up to 58 m.

In this paper, we report the accurate estimation of vine grape yield from a 3-D radar imagery technique. Three ground-based frequency-modulated continuous-wave radars operating, respectively, at 24, 77, and 122 GHz are used for the contact-less estimation of grape mass in vineyards. The 3-D 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%.

For better benefits and yields, a good estimation of the quantity of grapes in a vineyard is necessary. In this paper, a three-dimensional (3D) imagery technique using conventional 24 GHz frequency-modulated continuous- wave (FMCW) radar is applied for detecting and remotely estimating the intra-parcel quantity of grapes. An estimation is possible even in the presence of natural or artificial clutters such as leaves, wood, or irrigation hoses. The microwave sensing is performed from the radar beam scanning of a vineyard, and an estimator is defined to derive the quantity of grapes in grapevines from the radar echoes distribution in the interrogated 3D scene. An algorithm based on contour detection is applied to the 3D radar image and a new parameter, called the spread factor, is defined for classifying the echo levels of grapes. The quantity of grapes is finally deduced from an appropriate estimator. This remote sensing approach brings a new and flexible solution for precision viticulture by estimating the grape quantity even for grapes hidden by leaves.

This paper addresses the implementation of autonomous radiofrequency identification sensor nodes based on wireless power transfer. For size reduction, a switching method is proposed in order to use the same frequency band for both supplying power to the nodes and wirelessly transmitting the nodes’ data. A rectenna harvests the electromagnetic energy delivered by the dedicated radiofrequency source for charging a few-mF supercapacitor. For supercapacitors of 7 mF, it is shown that the proposed autonomous sensor nodes were able to wirelessly communicate with the reader at 868 MHz for 10 min without interruption for a tag-to-reader separation distance of 1 meter. This result was obtained from effective radiated powers of 2 W during the supercapacitor charging and of 100 mW during the wireless data communication.

This paper reports the yield estimation of vineyards from using a mobile millimeter-wave radar system. The proximal radar sensor is a 122GHz FM-CW radar mounted on a rover that moves along the furrows between vine plants. The proposed yield estimation is based on the classification of radar echoes measured for various grape varieties, for different meteorological conditions, and at different stages of grape development. As yield estimation errors are found to be lower than 10%, the proposed radar technique is suitable for precision viticulture applications.

This article describes the wireless and simultaneous interrogation and identification of multiple passive (zero-power) pressure sensors in an industrial environment with a reading range of at least 4 m. The 3-D beamscanning of the scene is performed from a 24-GHz frequency-modulated continuous-wave (FM-CW) radar and for diverse electric field polarizations. The identification is performed using a k-nearest neighbor classification. The benefit of using the cross-polarized electric fields combined with a radar imagery technique is enlightened from the analysis of the background clutter and the simultaneous remote interrogation of several passive pressure sensors at a distance up to 17.7 m. The measurement uncertainty on pressure obtained from the proposed long-range wireless technique is finally reported and discussed.