NDVI Point Cloud Generator Tool Using Low-Cost RGB-D Sensor

Calero, David; Fernández, Emilio; Parés, M. E.; Angelats, Eduard

doi:10.1109/iros.2018.8594175

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The benefits of using drone implementation have been identified [1] as: reduced measurement and data collection time, precision of measured data, increased yields, decreased costs of fertilizers and pesticides, and so on. To date, most applications have focused on outdoor operations, some applications are already commercialized, such as monitoring the health of crops by using Normalized Differential Vegetation Index (NDVI) in open fields [2] and greenhouses [3], detecting [4] and mapping [5] weeds, surveying the agricultural field [6], and spraying pesticides and fertilizers [7]. Quadcopters, typically small drones with four motors, with less air turbulence have attracted the interest of researchers for measuring environmental variables [8] in a greenhouse, such as temperature, humidity, CO2 concentration, and so on.…”

Section: Introductionmentioning

confidence: 99%

A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse

Huang

Tsay

Shiigi

et al. 2020

Sensors

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Quadcopters are beginning to play an important role in precision agriculture. In order to localize and operate the quadcopter automatically in complex agricultural settings, such as a greenhouse, a robust positioning system is needed. In previous research, we developed a spread spectrum sound-based local positioning system (SSSLPS) with a 20 mm accuracy within a 30 × 30 m greenhouse area. In this research, a noise tolerant SSSLPS was developed and evaluated. First, the acoustic noise spectrum emitted by the quadcopter was documented, and then the noise tolerance properties of SSSounds were examined and tested. This was done in a greenhouse with a fixed quadcopter (9.75 N thrust) with the positioning system mounted on it. The recorded quadcopter noise had a broadband noise compared to the SSSound. Taking these SSSound properties into account, the noise tolerance of the SSSLPS was improved, achieving a positioning accuracy of 23.2 mm and 31.6 mm accuracy within 12 × 6 m for both Time-division Multiple Access (TDMA) and Frequency-division Multiple Access (FDMA) modulation. The results demonstrate that the SSSLPS is an accurate, robust positioning system that is noise tolerant and can used for quadcopter operation even within a small greenhouse.

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