WHAT WE DO?

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Near-Infrared Spectroscopy (NIRs)

We use NIRs technology to predict organic composition of crops using advanced statistical methods and machine learning algorithms.

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X-Ray Fluorescence (XRF)

We use bench-top standard and hand-held XRF technology to estimate inorganic composition of cereals species grains. We develop calibrations for assessment of Fe and Zn in finger millet grains.

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Computer Tomography (CT)

We propose CT technology that appears invaluable to gain the time advantage in evaluation basic grain properties (grain size, shape, damage) on a structural level.

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OUR SENSORS

PHENOTYPED TRAITS

Priority traits identified in the crop improvement programs across CGIAR centers and sensor technologies being tested/deployed for their high-throughput assessment
Crop/quality Seed size Seed color Seed shape Cooking time Malting Shelling % Rancidity Moisture % IVOMD Protein % Oleic acid % Oil content % Fe [ppm] Zn [ppm] Ca [ppm] Aflatoxins As [ppm]
Chikpea
Cowpea
Lentil
Groundnut
Pigeonpea
Beans
Soybean
Finger millet
Sorghum
Pearl millet
Maize
Rice
Sensor technology Seed size Seed color Seed shape Cooking time Malting Shelling % Rancidity Moisture % IVOMD Protein % Oleic acid % Oil content % Fe[ppm] Zn[ppm] Ca[ppm] Aflatoxins As[ppm]
NIRs #
XRF
CT
Raman Spectra
Visible spectra

ACHIEVEMENTS

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6
Ongoing Projects
25000
Phenotyped Samples
25
Collaborating Institutes
190
Publications