Nondestructive Detection Method for the Calcium and Nitrogen Content of Living Plants Based on Convolutional Neural Networks (CNN) Using Multispectral Images

Grzegorz Kunstman, Paweł Kunstman, Łukasz Lasyk, Jacek Stanisław Nowak, Agnieszka Stępowska, Waldemar Kowalczyk, Jakub Dybaś and Ewa Szczęsny-Małysiak
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Grzegorz Kunstman: Active Text, 30-519 Krakow, Poland
Paweł Kunstman: Active Text, 30-519 Krakow, Poland
Łukasz Lasyk: Active Text, 30-519 Krakow, Poland
Jacek Stanisław Nowak: The National Institute of Horticultural Research, 96-100 Skierniewice, Poland
Agnieszka Stępowska: The National Institute of Horticultural Research, 96-100 Skierniewice, Poland
Waldemar Kowalczyk: The National Institute of Horticultural Research, 96-100 Skierniewice, Poland
Jakub Dybaś: Jagiellonian Centre for Experimental Therapeutics, Jagiellonian University, 30-348 Krakow, Poland
Ewa Szczęsny-Małysiak: Jagiellonian Centre for Experimental Therapeutics, Jagiellonian University, 30-348 Krakow, Poland

Agriculture, 2022, vol. 12, issue 6, 1-15

Abstract: Herein, we present the novel method targeted for determination of plant nutritional state with the use of computer vision and Neural Networks. The method is based on multispectral imaging performed by an exclusively designed Agroscanner and a dedicated analytical system for further data analysis with Neural Networks. An Agroscanner is a low-cost mobile construction intended for multispectral measurements at macro-scale, operating at four wavelengths: 470, 550, 640 and 850 nm. Together with developed software and implementation of a Neural Network it was possible to design a unique approach to process acquired plant images and assess information about plant physiological state. The novelty of the developed technology is focused on the multispectral, macro-scale analysis of individual plant leaves, rather than entire fields. Such an approach makes the method highly sensitive and precise. The method presented herein determines the basic physiological deficiencies of crops with around 80% efficiency.

Keywords: plant nutrients; spectral imaging; neural networks; computer vision (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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