Estimation of Total Nitrogen Content in Forage Maize ( Zea mays L.) Using Spectral Indices: Analysis by Random Forest

Magali J. López-Calderón, Juan Estrada-Ávalos, Víctor M. Rodríguez-Moreno, Jorge E. Mauricio-Ruvalcaba, Aldo R. Martínez-Sifuentes, Gerardo Delgado-Ramírez and Enrique Miguel-Valle
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Magali J. López-Calderón: Facultad de Agricultura y Zootecnia (FAZ-UJED), Universidad Juárez del Estado de Durango, Ejido Venecia, Tlahualilo Km 35, Gómez Palacio 35111, Mexico
Juan Estrada-Ávalos: INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico
Víctor M. Rodríguez-Moreno: INIFAP Campo Experimental Pabellón, Km 32.5, Carretera Ags-Zac, Pabellón de Arteaga 20660, Mexico
Jorge E. Mauricio-Ruvalcaba: INIFAP Campo Experimental Pabellón, Km 32.5, Carretera Ags-Zac, Pabellón de Arteaga 20660, Mexico
Aldo R. Martínez-Sifuentes: INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico
Gerardo Delgado-Ramírez: INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico
Enrique Miguel-Valle: INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico

Agriculture, 2020, vol. 10, issue 10, 1-15

Abstract: Knowing the total Nitrogen content (Nt) of forage maize ( Zea mays ) is important so that decisions can be made quickly and efficiently to adjust the timing and amount of both irrigation and fertilizer. In 2017 and 2018 during three growing cycles in two study plots, leaf samples were collected and the Dumas method was used to estimate Nt. During the same growing seasons and on the same sampling plots, a Parrot Sequoia camera mounted on an unmanned aerial vehicle (UAV) was used to collect high resolution images of forage maize study plots. Thirteen multispectral indices were generated and, from these, a Random Forest (RF) algorithm was used to estimate Nt. RF is a machine-learning technique and is designed to work with extremely large datasets. Overall analysis showed five of the 13 indices as the most important. One of these five, the Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index, was found to be the most important for estimation of Nt in forage maize (R 2 = 0.76). RF handled the complex dataset in a time-efficient manner and Nt did not differ significantly when compared between traditional methods of evaluating Nt at the canopy level and using UAVs and RF to estimate Nt in forage maize. This result is an opportunity to explore many new research options in precision farming and digital agriculture.

Keywords: nitrogen content; remote sensing; spectral indices; random forest (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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