Publications
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Pear Flower Cluster Quantification Using RGB Drone Imagery KU Leuven
High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of ...
Design and Test of a Low-Cost RGB Sensor for Online Measurement of Microalgae Concentration within a Photo-Bioreactor KU Leuven
In this study, a low-cost RGB sensor is developed to measure online the microalgae concentration within a photo-bioreactor. Two commercially available devices, i.e., a spectrophotometer for offline measurements and an immersed probe for online measurements, are used for calibration and comparison purposes. Furthermore, the potential of such a sensor for estimating other variables is illustrated with the design of an extended Luenberger observer.
Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter Ghent University University of Antwerp
Uniform Aerosol Jet printed polymer lines with 30 mu m width for 140 ppi resolution RGB organic light emitting diodes KU Leuven
We demonstrate the patterning of top emitting organic light emitting diodes (OLEDs) by direct-write Aerosol Jet printing in air from non-halogenated solvents. Indane was determined to be a suitable single solvent to dissolve the archetypal host poly(N-vinylcarbazole) and guest emitting dopants complexes for red, green, and blue OLEDs, and to print on both PEDOT:PSS and MoO3 transport layers. The Aerosol Jet deposition parameters were studied, ...
Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models Interuniversity Microelectronics Centre
Background Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models ...
Iterative online 3D reconstruction from RGB images Ghent University
RGB Colors and Ecological Optics KU Leuven
Integrating spectral and textural information for identifying the tasseling date of summer maize using UAV based RGB images University of Antwerp
The extraction of phenological events in forest and agriculture commonly relies on Vegetation Indices (VI) composed by visible and near infrared bands from satellite images. However, the textural information playing an important role in image fusion, image classification and change detection is commonly ignored. In this study, high-throughput images collected from an Unmanned Aerial Vehicle (UAV) platform during the growth stages of summer maize ...