Martínez-Macias, K.J.; Martínez-Sifuentes, A.R.; Márquez-Guerrero, S.Y.; Reyes-González, A.; Preciado-Rangel, P.; Yescas-Coronado, P.; Trucíos-Caciano, R. Machine-Learning Approaches in N Estimations of Fig Cultivations Based on Satellite-Born Vegetation Indices. Nitrogen2024, 5, 598-609.
Martínez-Macias, K.J.; Martínez-Sifuentes, A.R.; Márquez-Guerrero, S.Y.; Reyes-González, A.; Preciado-Rangel, P.; Yescas-Coronado, P.; Trucíos-Caciano, R. Machine-Learning Approaches in N Estimations of Fig Cultivations Based on Satellite-Born Vegetation Indices. Nitrogen 2024, 5, 598-609.
Martínez-Macias, K.J.; Martínez-Sifuentes, A.R.; Márquez-Guerrero, S.Y.; Reyes-González, A.; Preciado-Rangel, P.; Yescas-Coronado, P.; Trucíos-Caciano, R. Machine-Learning Approaches in N Estimations of Fig Cultivations Based on Satellite-Born Vegetation Indices. Nitrogen2024, 5, 598-609.
Martínez-Macias, K.J.; Martínez-Sifuentes, A.R.; Márquez-Guerrero, S.Y.; Reyes-González, A.; Preciado-Rangel, P.; Yescas-Coronado, P.; Trucíos-Caciano, R. Machine-Learning Approaches in N Estimations of Fig Cultivations Based on Satellite-Born Vegetation Indices. Nitrogen 2024, 5, 598-609.
Abstract
Nitrogen is one of the most important macronutrients for crops, and in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation ≥ 0.7 were selected for subsequent use in Random Forest, Gradient Boosting, and Artificial Neural Networks to determine their relationship with nitrogen levels measured in the laboratory. Random Forest showed no relationship, yielding an R2 of zero, whereas Artificial Neural Networks yielded the best results with an R2 of 0.93. Thus, it is reliable to estimate nitrogen levels using this algorithm by feeding it with data from TCARI, MCARI, TCARI/OSAVI, and MCARI/OSAVI, assisted by technological tools.
Keywords
Gradient Boosting; Random Forest; Artificial Neural Networks; Vegetation Index
Subject
Environmental and Earth Sciences, Soil Science
Copyright:
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