A5054765358- Spectroscopy and Chemometric Analyses
- Horticultural and Viticultural Research
- Plant Physiology and Cultivation Studies
- Postharvest Quality and Shelf Life Management
- Remote Sensing in Agriculture
- Smart Agriculture and AI
- Leaf Properties and Growth Measurement
- Phytochemicals and Antioxidant Activities
- Water Quality Monitoring and Analysis
- Advanced Chemical Sensor Technologies
- Remote Sensing and LiDAR Applications
- Plant Water Relations and Carbon Dynamics
- Irrigation Practices and Water Management
- Spectroscopy Techniques in Biomedical and Chemical Research
- Tree Root and Stability Studies
- Date Palm Research Studies
- Essential Oils and Antimicrobial Activity
- Edible Oils Quality and Analysis
- Greenhouse Technology and Climate Control
- Plant Reproductive Biology
- Meat and Animal Product Quality
- Fermentation and Sensory Analysis
- Plant responses to water stress
- Plant Surface Properties and Treatments
- Optical Imaging and Spectroscopy Techniques
Leibniz Institute for Agricultural Engineering and Bioeconomy
2013-2024
Leibniz Association
2007-2015
Berliner Hochschule für Technik
2014
AustriaTech (Austria)
2012
Leibniz Institute for Neurobiology
2010
Humboldt-Universität zu Berlin
1997-2001
Florida Department of Citrus
2001
University of Florida
2001
The application of visible (Vis; 400–750 nm) and near infrared red (NIR; 750–2500 region spectroscopy to assess fruit vegetables is reviewed in context 'point' spectroscopy, as opposed multi- or hyperspectral imaging. Vis targets colour assessment pigment analysis, while NIR has been applied macro constituents (principally water) fresh produce commercial practice, a wide range attributes the scientific literature. This review focusses key issues relevant widespread implementation Vis-NIR...
Yield monitoring systems in fruit production mostly rely on color features, making the discrimination of fruits challenging due to varying light conditions. The implementation geometric and radiometric features three-dimensional space (3D) analysis can alleviate such difficulties improving detection. In this study, a detection range (LiDAR) system was used scan apple trees before (TL) after defoliation (TD) four times during seasonal tree growth. An method based calibrated apparent...
A feasible method to analyse fruit at the tree is requested in precise production management. The employment of light detection and ranging (LiDAR) was approached aimed measuring number fruit, quality-related size, ripeness-related chlorophyll skin. During development (65 – 130 day after full bloom, DAFB), apples were harvested analysed laboratory (n = 225) with two LiDAR laser scanners 660 905 nm. From these 3D point clouds, normalized difference vegetation index (NDVILiDAR) calculated....
In the dataset presented in this article, samples belonging to one of following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras visible/near-infrared spectral domain (430-900 nm). The was compiled putting together measurements from different calibrated imaging crops facilitate training artificial intelligence models, helping overcome generalization problem models. particular, focuses on estimating dry matter content across various a single model...
Non-destructive sensing of fruit chlorophyll appears feasible using spectroscopy in the visible wavelength range. However, postharvest application, measurements should even be carried out contact-free for avoiding disturbances produce and enabling automated measurements. In present work a light detection ranging sensor (LiDAR) laser scanner, emitting at 660 nm, was employed to analyse banana (n = 180) with 0.9 m distance during ripening 8 d. The intensity echo recorded each hit, subsequently...
Remote sensing in agriculture aims to search new methods monitor fruit at the tree, thus improving estimation of yield-related variables. Light detection and range (LiDAR) scanning was introduced obtain geometric radiometric information from surfaces by means 3D point clouds. A model estimate apple size segmented cloud is proposed present study. The consists approximation clouds a reference shape given 2D Fourier series expansion. Each approximated its using an iterative error minimization...