Improving Nitrogen Status Estimation in Malting Barley Based on Hyperspectral Reflectance and Artificial Neural Networks

0552700 - ÚVGZ 2022 RIV CH eng J - Journal Article
Klem, Karel - Křen, J. - Simor, J. - Kováč, Daniel - Holub, Petr - Míša, P. - Svobodová, I. - Lukáš, V. - Lukeš, Petr - Findurová, Hana - Urban, Otmar
Improving Nitrogen Status Estimation in Malting Barley Based on Hyperspectral Reflectance and Artificial Neural Networks.
Agronomy. Roč. 11, č. 12 (2021), č. článku 2592. E-ISSN 2073-4395
R&D Projects: GA MŠMT(CZ) EF16_019/0000797; GA MZe(CZ) QK1910197
Institutional support: RVO:86652079
Keywords : partial least-squares * vegetation indexes * spring barley * chlorophyll content * remote estimation * winter-wheat * grain-yield * growth * crop * nutrition * artificial neural network * grain yield * Hordeum vulgare * nitrogen status * hyperspectral reflectance
OECD category: Agriculture
Impact factor: 3.949, year: 2021
Method of publishing: Open access
https://www.mdpi.com/2073-4395/11/12/2592

Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011-2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400-490, 530-570, and 710-720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.
Permanent Link: http://hdl.handle.net/11104/0327827