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

Agriculture

卷:10

MagaliJ. López-Calderón¹  Enrique Miguel-Valle²  AldoR. Martínez-Sifuentes²  Juan Estrada-Ávalos²  Gerardo Delgado-Ramírez²  VíctorM. Rodríguez-Moreno³  JorgeE. Mauricio-Ruvalcaba³ 

[1] Facultad de Agricultura y Zootecnia (FAZ-UJED), Universidad Juárez del Estado de Durango, Ejido Venecia, Tlahualilo Km 35, Gómez Palacio 35111, Mexico;

[2] INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico;

[3] INIFAP Campo Experimental Pabellón, Km 32.5, Carretera Ags-Zac, Pabellón de Arteaga 20660, Mexico;

关键词: nitrogen content;    remote sensing;    spectral indices;    random forest;   

DOI  :  10.3390/agriculture10100451

来源: DOAJ

【 摘 要 】

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 (R² = 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.

【 授权许可】

Unknown