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

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

Keywords

• artificial neural networks
• gradient boosting
• nitrogen
• random forest
• remote sensing
• vegetation index