Photoluminescent Sensor of Scarification Efficiency of Fodder Plants' Seeds.
Mikhail V BelyakovPublished in: Sensors (Basel, Switzerland) (2022)
Optoelectronic sensors open up new possibilities for predicting the yield for their possible correction, including increasing the seed germination of forage plants. The luminescent properties of unscarified and scarified seeds of various germination galega, clover and alfalfa are compared. The dependence of germination on the photoluminescence flux is approximated by linear equations with a determination coefficient R 2 = 0.932-0.999. A technological process for analyzing the scarification quality of forage seed plants is proposed, including sample preparation, photoluminescence excitation and registration, amplification of the received electrical signal and determination of germination based on calibration equations. This is followed by a decision on sowing, or re-scarification. The scheme of the scarification quality control device has been developed for which the LED, as well as the radiation receiver and other elements, has been selected according to the energy efficiency criterion. Mechanical scarification of the forage plants' seed surfaces has a significant effect on their photoluminescent properties. The flux increases by 1.5-1.7 times for galega, 2.0-3.0 times for clover and 2.3-3.9 times for alfalfa. Linear approximation of the flux dependence on germination with a high coefficient of determination allows us to obtain reliable linear calibration equations. Preliminary mock-up laboratory tests allow us to talk about the developed method's effectiveness and device.
Keyphrases
- quantum dots
- molecularly imprinted
- quality control
- plant growth
- solid phase extraction
- energy transfer
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- randomized controlled trial
- systematic review
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- magnetic resonance imaging
- computed tomography
- diffusion weighted imaging
- radiation therapy
- escherichia coli
- staphylococcus aureus
- decision making
- magnetic resonance
- pseudomonas aeruginosa
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- mass spectrometry
- simultaneous determination
- neural network
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- label free