Our competences

Photo: Manuel Gutjahr

WG Precision horticulture

The WG Precision Horticulture develops methods for acquiring in-situ sensor data of fruit or vegetables aimed at more precise process management in the production and postharvest.

The group works on
(i) the extraction of product data from digital sensor signals, and
(ii) the extension of agronomic models by means of digital product information.

Our applications capture product data related to abiotic stress, e.g. due to changes in global radiation, temperature, ethylene exposure. Precisely adapting production processes to the needs of the crop reduces the consumption of resources, while continuing to enable high yields. For example, optimising thinning intensity in fruit production has been shown to reduce the use of chemicals while increasing the yield per hectare by 5 tonnes of apples.  In sorting, storage, and shelf life, sensor data can help to keep product quality of the perishable, fresh produce. Consequently, digital product information can help to optimize processes along the supply chain.

Research equipment

  • Thin-film sensor for mechanical load
  • Texture Analyzer
  • Dynamic load sensor to implant in fruits
  • Temperature corrected refractometer
  • Multi-spectral refractometer
  • Electro-chemical gas sensors
  • Oxygen micro sensor system
  • GC
  • U-HPLC
  • Fluorescence spectroscopy
  • Time-resolved fluorescence spectrometry
  • Vis/NIR spectrophotometry (190 nm – 2500 nm) in various optical geometries
  • Calibration stand (wavelength and intensity) for Vis/NIRS
  • Optical bench
  • Laser-induced spatially resolved spectroscopy (SRS)
  • Tool for photometric fruit pigment analysis
  • Terrestrial mobile LiDAR laser scanners
  • Calibration stand (geometric and intensity) for LiDAR laser scanner
  • Thermal cameras
  • Hyperspectral cameras
  • 3D time-of-flight camera
  • Scholander bomb
  • Sapflow meter
  • dendrometer
  • IRGA porometer
  • Chlorophyll fluorescence kinetic

Free software tools developed by the group:
https://cran.r-project.org/package=MCBackscattering (R Code for Monte-Carlo Simulation on backscattering data)
https://github.com/ATB-Potsdam/Matlab_TVDI_cal (Matlab Code for calculating land surface temperature and vegetation indices from Landsat 8 satellite)
https://play.google.com/store/apps/details?id=com.cpinfo.fiorama (mobile App to record time series of fruit development)
https://play.google.com/store/apps/details?id=de.atb_potsdam.www.cherryharvest (mobile App for analyzing the cherry fruit growth)

Group leader

Team of the WG

M.Sc. Nicolás Tapia Zapata
M.Sc. Chong Shi

Guest scientists, students:
M.Sc. Kowshik Kumar Saha (Bangladesh Agricultural Research Institute, Bangladesh)
M.Sc. Xue An (Northwest A&F University, P.R. China)
M.Sc. Marco Bignardi (Geisenheim University, Germany)
M.Sc. Yang Zhou (Zoe) (Massey University, New Zealand)
M.Sc. Sven Jörissen (University Würzburg, Germany)
M.Sc. Ekaterine Burkadze (Agriculture University of Georgia)
Veronika Schöniger (Hochschule für nachhaltige Entwicklung Eberswalde, Germany)
Arad Hilel (Technische Universität Berlin, Germany)


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