Machine-Learning Approaches in N Estimations of Fig Cultivations Based on Satellite-Born Vegetation Indices

Nitrogen Pub Date : 2024-07-10 DOI:10.3390/nitrogen5030040

Karla Janeth Martínez-Macias, A. R. Martínez-Sifuentes, Selenne Yuridia Márquez-Guerrero, Arturo Reyes-González, P. Preciado-Rangel, Pablo Yescas-Coronado, Ramón Trucíos-Caciano

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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 of ≥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; and gradient boosting and the classical method were similar with 0.7; whereas artificial neural networks yielded the best results with an R2 of 0.93. Thus, estimating nitrogen levels using this algorithm is reliable, by feeding it with data from the Modified Chlorophyll Absorption Ratio Index, Transformed Chlorophyll Absorption Reflectance Index, Modified Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index, and Transformed Chlorophyll Absorption Ratio Index/Optimized Soil Adjusted Vegetation Index

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基于卫星植被指数的无花果种植氮估算机器学习方法

氮是农作物最重要的宏量营养元素之一，结合人工智能算法，可以借助遥感植被指数对其进行估算。通过计算各种指数，选出相关性≥0.7 的指数，然后将其用于随机森林、梯度提升和人工神经网络，以确定它们与实验室测量的氮含量之间的关系。随机森林没有显示出任何关系，R2 为零；梯度提升法和经典方法的 R2 为 0.7，结果类似；而人工神经网络的结果最好，R2 为 0.93。因此，通过向该算法提供修正叶绿素吸收比指数、转化叶绿素吸收反射比指数、修正叶绿素吸收比指数/优化土壤调整植被指数和转化叶绿素吸收比指数/优化土壤调整植被指数的数据，使用该算法估算氮含量是可靠的。

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Nitrogen

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