Version 1
: Received: 6 May 2024 / Approved: 8 May 2024 / Online: 9 May 2024 (12:15:23 CEST)
How to cite:
Martínez-Macias, K. J.; Martínez-Sifuentes, A. R.; Márquez-Guerrero, S. Y.; Reyes-González, A.; Preciado-Rangel, P.; Yescas-Coronado, P.; Trucíos-Caciano, R. Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning. Preprints2024, 2024050476. https://doi.org/10.20944/preprints202405.0476.v1
Martínez-Macias, K. J.; Martínez-Sifuentes, A. R.; Márquez-Guerrero, S. Y.; Reyes-González, A.; Preciado-Rangel, P.; Yescas-Coronado, P.; Trucíos-Caciano, R. Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning. Preprints 2024, 2024050476. https://doi.org/10.20944/preprints202405.0476.v1
Martínez-Macias, K. J.; Martínez-Sifuentes, A. R.; Márquez-Guerrero, S. Y.; Reyes-González, A.; Preciado-Rangel, P.; Yescas-Coronado, P.; Trucíos-Caciano, R. Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning. Preprints2024, 2024050476. https://doi.org/10.20944/preprints202405.0476.v1
APA Style
Martínez-Macias, K. J., Martínez-Sifuentes, A. R., Márquez-Guerrero, S. Y., Reyes-González, A., Preciado-Rangel, P., Yescas-Coronado, P., & Trucíos-Caciano, R. (2024). Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning. Preprints. https://doi.org/10.20944/preprints202405.0476.v1
Chicago/Turabian Style
Martínez-Macias, K. J., Pablo Yescas-Coronado and Ramón Trucíos-Caciano. 2024 "Nitrogen Estimation in Fig Cultivation through Remote Sensing and Machine Learning" Preprints. https://doi.org/10.20944/preprints202405.0476.v1
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:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
