Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T05:44:55.781Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting protein content of silage maize using remotely sensed multispectral imagery and proximal leaf sensing

Published online by Cambridge University Press:  04 November 2022

Nikrooz Bagheri Open the ORCID record for Nikrooz Bagheri [Opens in a new window] ,
Maryam Rahimi Jahangirlou  and
Mehryar Jaberi Aghdam
Nikrooz Bagheri*
Affiliation:
Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
Maryam Rahimi Jahangirlou
Affiliation:
Department of Agronomy and Plant Breeding Sciences, College of Aburaihan, University of Tehran, Tehran, Iran
Mehryar Jaberi Aghdam
Affiliation:
Department of Agronomy, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran
*
*Corresponding Author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Timely estimation of silage maize protein provides an effective decision to adapt optimized strategies for nitrogen fertilizer management and also harvesting time for farmers. So, this research aimed to investigate whether using vegetative indices (VIs) derived from UAV remotely sensed multispectral (with 520–900 nm wavelengths) imagery and also Soil Plant Analysis Development (SPAD) greenness index can be used to detect the leaf protein concentration (LPC) of silage maize, as a function of various nitrogen rates (0, 50, 100, and 150% of recommended dosage). Results of principal component analysis (PCA) suggested that LPC was highly correlated with leaf greenness index in all developmental stages. In addition, LPC was highly correlated with most of the VIs investigated. A PCA clustering showed the meaningful pattern of N rates. Higher LPC values, VIs, and greenness index were more concentrated in the higher nitrogen (N100% and N150%) sectors. Nitrogen Reflectance Index (NRI) was identified as the most important VIs to monitor and predict LPC in the silage maize field, showing a strong polynomial relationship with LPC in both eight-leaf collar (V8) (R2 = 0.81, p ≤ 0.01) and tasseling (VT) (R2 = 0.98, p ≤ 0.001) stages. In addition, among VIs, the Normalized Difference Vegetation Index (NDVI) demonstrated a significant linear regression relationship with LPC (R2 = 0.80, p ≤ 0.01) in the VT. Findings suggested the high potential of VIs extracted by UAV-taken multispectral imagery and also SPAD proximal sensing to help farmers rapidly diagnose LPC in silage maize, in line with the objectives of precision farming.

Keywords

NitrogenRemote sensingPrecision agricultureProteinUnmanned Aerial Vehicle (UAV)
Type
Research Article
Information
Experimental Agriculture , Volume 58 , 2022 , e41
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Argento, F., Anken, T., Abt, F., Vogelsanger, E., Walter, A. and Liebisch, F. (2021). Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data. Precision Agriculture 22(2), 364–386.CrossRefGoogle Scholar
Bagheri, N. (2016). Development of a high-resolution aerial remote-sensing system for precision agriculture. International Journal of Remote Sensing 38 (8–10), 2053–2065.CrossRefGoogle Scholar
Bagheri, N., Ahmadi, H., Alavipanah, S. K. and Omid, M. (2012). Soil-Line vegetation indices for corn nitrogen content prediction. International Agrophysics 26, 103–108.CrossRefGoogle Scholar
Bagheri, N., Ahmadi, H., Alavipanah, S. K., and Omid, M. (2013). Multispectral remote sensing for site-specific nitrogen fertilizer management. Pesquisa Agropecuária Brasileira 48, 1394–1401.CrossRefGoogle Scholar
Cao, X., Sun, H., Wang, C., Ren, X., Liu, H. and Zhang, Z. (2018). Effects of late-stage nitrogen fertilizer application on the starch structure and cooking quality of rice. Journal of the Science of Food and Agriculture 98(6), 2332–2340.CrossRefGoogle ScholarPubMed
Caturegli, L., Corniglia, M., Gaetani, M., Grossi, N., Magni, S., Migliazzi, M., Angelini, L., Mazzoncini, M., Silvestri, N., Fontanelli, M., Raffaelli, M., Peruzzi, A. and Volterrani, M. (2016). Unmanned aerial vehicle to estimate nitrogen status of turfgrasses. PloS One 11(6), e0158268.CrossRefGoogle ScholarPubMed
Chen, X., Dong, Z., Liu, J., Wang, H., Zhang, Y., Chen, T., … Du, Y., Shao, L. and Xie, J. (2020). Hyperspectral characteristics and quantitative analysis of leaf chlorophyll by reflectance spectroscopy based on a genetic algorithm in combination with partial least squares regression. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 243, 118786.CrossRefGoogle ScholarPubMed
Corti, M., Cavalli, D., Cabassi, G., Vigoni, A., Degano, L. and Gallina, P.M. (2019). Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables. Precision Agriculture 20(4), 675–696.CrossRefGoogle Scholar
Darby, H.M. and Lauer, J.G. (2002). Harvest date and hybrid influence on corn forage yield, quality, and preservation. Agronomy Journal 94(3), 559–566.CrossRefGoogle Scholar
Devadas, R., Lamb, D.W., Simpfendorfer, S. and Backhouse, D. (2009). Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture 10(6), 459–470.CrossRefGoogle Scholar
Diker, K. and Bausch, W.C. (2003). Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosystems Engineering 85(4), 437–447.CrossRefGoogle Scholar
Galieni, A., D’Ascenzo, N., Stagnari, F., Pagnani, G., Xie, Q. and Pisante, M. (2020). Past and future of plant stress detection: an overview from remote sensing to Positron Emission Tomography. Frontiers in Plant Science 11, 1975.Google ScholarPubMed
Gitelson, A.A., Chivkunova, O.B. and Merzlyak, M.N. (2009). Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. American Journal of Botany 96(10), 1861–1868.CrossRefGoogle ScholarPubMed
Gous, P.W., Warren, F., Mo, O.W., Gilbert, R.G. and Fox, G.P. (2015). The effects of variable nitrogen application on barley starch structure under drought stress. Journal of the Institute of Brewing 121(4), 502–509.CrossRefGoogle Scholar
Guebel, D.V., Nudel, B.C. and Giulietti, A.M. (1991). A simple and rapid micro-Kjeldahl method for total nitrogen analysis. Biotechnology Techniques 5(6), 427–430.CrossRefGoogle Scholar
Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J. and Strachan, I.B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90(3), 337–352.CrossRefGoogle Scholar
Hatfield, J.L., Gitelson, A.A., Schepers, J.S. and Walthall, C.L. (2008). Application of spectral remote sensing for agronomic decisions. Agronomy Journal 100(3), 117–131.CrossRefGoogle Scholar
Jaberi-Aghdam, M., Momayezi, M.R., Bagheri, N., Azizi, P. and Nasri, M. (2020). Detection of excess nitrogen stress of corn and hazards with aerial multi-spectral imaging by UAV. Environmental Management Hazards 7(2), 163–180. (In Persian with English abstract).Google Scholar
Klopfenstein, T.J., Erickson, G.E. and Berger, L.L. (2013). Maize is a critically important source of food, feed, energy and forage in the USA. Field Crops Research 153, 5–11.CrossRefGoogle Scholar
Li-Hong, X.U.E., Wei-Xing, C.A.O. and Lin-Zhang, Y.A.N.G. (2007). Predicting grain yield and protein content in winter wheat at different N supply levels using canopy reflectance spectra. Pedosphere 17(5), 646–653.Google Scholar
Lin, F.F., Deng, J.S., Shi, Y.Y., Chen, L.S. and Wang, K. (2010). Investigation of SPAD meter-based indices for estimating rice nitrogen status. Computers and Electronics in Agriculture 71, S60–S65.CrossRefGoogle Scholar
López-Calderón, M.J., Estrada-Ávalos, J., Rodríguez-Moreno, V.M., Mauricio-Ruvalcaba, J.E., Martínez-Sifuentes, A.R., Delgado-Ramírez, G. and Miguel-Valle, E. (2020). Estimation of total Nitrogen content in forage maize (Zea mays L.) using spectral indices: Analysis by random forest. Agriculture 10(10), 451.CrossRefGoogle Scholar
Mandić, V., Bijelić, Z., Krnjaja, V., Simić, A., Petričević, M., Mićić, N. and Caro-Petrović, V. (2018). Effect of harvesting time on forage yield and quality of maize. Biotechnology in Animal Husbandry 34(3), 345–353.CrossRefGoogle Scholar
Nasielski, J., Earl, H. and Deen, B. (2019). Luxury vegetative nitrogen uptake in maize buffers grain yield under post-silking water and nitrogen stress: A mechanistic understanding. Frontiers in Plant Science 10, 318.CrossRefGoogle ScholarPubMed
Nguy-Robertson, A.L. (2013). The mathematical identity of two vegetation indices: MCARI2 and MTVI2. International Journal of Remote Sensing 34(21), 7504–7507.CrossRefGoogle Scholar
Osco, L.P., Junior, J.M., Ramos, A.P.M., Furuya, D.E.G., Santana, D.C., Teodoro, L.P.R. and Teodoro, P.E. (2020). Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques. Remote Sensing 12(19), 3237.CrossRefGoogle Scholar
Padilla, F.M., de Souza, R., Peña-Fleitas, M.T., Gallardo, M., Gimenez, C. and Thompson, R.B. (2018b). Different responses of various chlorophyll meters to increasing nitrogen supply in sweet pepper. Frontiers in Plant Science 9, 1752.CrossRefGoogle ScholarPubMed
Padilla, F.M., Gallardo, M., Peña-Fleitas, M.T., De Souza, R. and Thompson, R.B. (2018a). Proximal optical sensors for nitrogen management of vegetable crops: A review. Sensors 18(7), 2083.CrossRefGoogle ScholarPubMed
Pettersson, C.G. and Eckersten, H. (2007). Prediction of grain protein in spring malting barley grown in northern Europe. European Journal of Agronomy 27(2–4), 205–214.CrossRefGoogle Scholar
Pettersson, C.G., Söderström, M. and Eckersten, H. (2006). Canopy reflectance, thermal stress, and apparent soil electrical conductivity as predictors of within-field variability in grain yield and grain protein of malting barley. Precision Agriculture 7(5), 343–359.CrossRefGoogle Scholar
Rahimi Jahangirlou, M., Akbari, G.A., Alahdadi, I., Soufizadeh, S., Kumar, U. and Parsons, D. (2021b). Phenotypic traits, grain yield and yield components of Maize cultivars under combinations of management practices in semi-arid conditions of Iran. International Journal of Plant Production 15(3), 459–471.CrossRefGoogle Scholar
Rahimi Jahangirlou, M., Akbari, G.A., Alahdadi, I., Soufizadeh, S., Ludemann, C. and Parsons, D. (2022). Phenotypic predictors of dent maize grain quality based on different genetics and management practices. Journal of Cereal Science, 103388.CrossRefGoogle Scholar
Rahimi Jahangirlou, M., Akbari, G.A., Alahdadi, I., Soufizadeh, S. and Parsons, D. (2021a). Grain quality of maize cultivars as a function of planting dates, irrigation and Nitrogen stress: A case study from semiarid conditions of Iran. Agriculture 11(1), 11.CrossRefGoogle Scholar
Reum, D. and Zhang, Q. (2007). Wavelet based multi-spectral image analysis of maize leaf chlorophyll content. Computers and Electronics in Agriculture 56(1), 60–71.CrossRefGoogle Scholar
Shendryk, Y., Sofonia, J., Garrard, R., Rist, Y., Skocaj, D. and Thorburn, P. (2020). Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging. International Journal of Applied Earth Observation and Geoinformation 92, 102177.CrossRefGoogle Scholar
Smith, A.M., Bourgeois, G., Teillet, P.M., Freemantle, J. and Nadeau, C. (2008). A comparison of NDVI and MTVI2 for estimating LAI using CHRIS imagery: A case study in wheat. Canadian Journal of Remote Sensing 34(6), 539–548.CrossRefGoogle Scholar
Wang, L.W. and Wei, Y.X. (2013). Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing. Spectroscopy and Spectral Analysis 33(10), 2823–2827.Google ScholarPubMed
Xiong, D., Chen, J., Yu, T., Gao, W., Ling, X., Li, Y., … Huang, J. (2015). SPAD-based leaf nitrogen estimation is impacted by environmental factors and crop leaf characteristics. Scientific Reports 5(1), 1–12.CrossRefGoogle ScholarPubMed
Xu, X., Fan, L., Li, Z., Meng, Y., Feng, H., Yang, H. and Xu, B. (2021). Estimating leaf Nitrogen content in corn based on information fusion of multiple-sensor imagery from UAV. Remote Sensing 13(3), 340.CrossRefGoogle Scholar
Zhao, H., Song, X., Yang, G., Li, Z., Zhang, D. and Feng, H. (2019). Monitoring of nitrogen and grain protein content in winter wheat based on Sentinel-2A data. Remote Sensing 11(14), 1724.CrossRefGoogle Scholar
Supplementary material: File

Bagheri et al. supplementary material

Figures S1-S2

Download Bagheri et al. supplementary material(File)
File 1.3 MB