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David Nuyttens

Researcher at Flemish Community

Publications -  171
Citations -  3486

David Nuyttens is an academic researcher from Flemish Community. The author has contributed to research in topics: Sprayer & Spray nozzle. The author has an hindex of 28, co-authored 161 publications receiving 2808 citations. Previous affiliations of David Nuyttens include Katholieke Universiteit Leuven.

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Effect of nozzle type, size and pressure on spray droplet characteristics

TL;DR: In this article, the authors developed a test rig and protocol for the characterisation of spray nozzles using a phase doppler particle analyser (PDPA), which was able to measure droplet sizes and velocities based on light-scattering principles.
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The influence of operator-controlled variables on spray drift from field crop sprayers

TL;DR: In this article, the authors measured the amount of sedimenting drift from a horizontal boom sprayer for different spray application techniques under field conditions and compared the results with the results from a reference spray, taking into account variations in meteorological conditions during the field drift experiments.
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Pesticide-laden dust emission and drift from treated seeds during seed drilling: a review

TL;DR: The state of the art of the phenomenon of dust emission and drift from pesticide seed dressing during sowing and its consequences is presented and possible mitigation measures are discussed.
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Droplet Size and Velocity Characteristics of Agricultural Sprays

TL;DR: In this article, the authors measured droplet sizes and velocities at 0.50 m below the nozzle using a PDPA laser-based measurement setup, and the effects of nozzle type (standard, low-drift, and air-inclusion), nozzle size (ISO 02, 03, 04, and 06) and operating pressure (2.0, 3.0 and 4.0 bar) were tested.
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Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery

TL;DR: In this paper, a hyperspectral snapshot mosaic camera was used for weed and maize classification, and an image processing, feature engineering and machine learning techniques were discussed when developing an optimal classification model for the three kinds of weeds and maize.