Open Access
21 October 2020 Vis–NIR spectroscopy: from leaf dry mass production estimate to the prediction of macro- and micronutrients in soybean crops
Marlon Rodrigues, Marcos Rafael Nanni, Everson Cezar, Glaucio Leboso Alemparte Abran dos Santos, Amanda Silveira Reis, Karym Mayara de Oliveira, Roney Berti de Oliveira
Author Affiliations +
Abstract

Our work aimed to evaluate the use of visible–near-infrared (Vis–NIR) spectroscopy for predicting the production of leaf dry mass (LDM), as well as macro- and micronutrients contents of soybean leaves grown after application of limestone-mining coproducts. The treatments were arranged within a triple factorial scheme (6  ×  2  ×  2  +  2) and placed into pots in a greenhouse. We evaluated the following factors: type of input (limestone-mining coproducts), input particle size (filler and powder), and soil class (Arenosol and Ferralsol). After inputs incubation, the soybean was sown. Then, 42 days after sowing, we collected the foliar spectra, as well as leaves, for further analysis of the contents of macro- and micronutrients in leaves and production of LDM. We managed to adjust models at the stage of prediction with R2p  >  0.50 and RPDp  >  1.50 for the variables LDM, P, K, Mg, S, and Zn, with emphasis on the first four, which presented R2p above 0.65. Therefore, we conclude that Vis–NIR spectroscopy has a potential for predicting LDM and the nutrients contents of soybean subjected to the application of limestone-mining coproducts, with advantages such as speed, low cost, and no use of reagents that are toxic to the environment.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Marlon Rodrigues, Marcos Rafael Nanni, Everson Cezar, Glaucio Leboso Alemparte Abran dos Santos, Amanda Silveira Reis, Karym Mayara de Oliveira, and Roney Berti de Oliveira "Vis–NIR spectroscopy: from leaf dry mass production estimate to the prediction of macro- and micronutrients in soybean crops," Journal of Applied Remote Sensing 14(4), 044505 (21 October 2020). https://doi.org/10.1117/1.JRS.14.044505
Received: 5 July 2020; Accepted: 12 October 2020; Published: 21 October 2020
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CITATIONS
Cited by 14 scholarly publications.
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KEYWORDS
Spectroscopy

Magnesium

Calcium

Manganese

Zinc

Iron

Calibration

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