What question did this study set out to answer?

This work aims to predict soil salinity and sodicity using aerial images and machine learning techniques.

June 20, 2026Open Access

Salinization evaluation in arable land using unmanned aerial vehicles, multispectral images, and machine learning

PHPatricia Paulina Hernández-VictoriaColegio de Postgraduados HFHéctor Flores-MagdalenoColegio de Postgraduados AQAbel Quevedo-NolascoColegio de Postgraduados

Key Points

This work aims to predict soil salinity and sodicity using aerial images and machine learning techniques.
Collected 300 soil samples to measure electrical conductivity and exchangeable sodium percentage.
Used multispectral images from UAVs to calculate reflectance indices for predicting salinity.
Trained and validated five machine learning algorithms: Neural Networks, Random Forest, Support Vector Machine, Nearest Neighbors, and Decision Trees.
Random Forest model achieved 69% accuracy, 69% recall, and 39% kappa for predicting electrical conductivity categories.
Support Vector Machine provided 73% accuracy, 65% recall, and 41% kappa for predicting exchangeable sodium percentage.

Abstract

Soil salinity reduces crop productivity and promotes desertification.Traditional methods to assess soil salinity are costly and require a lot of time and effort.Salinity diagnosis could be more efficient by processing aerial images.This work aimed to predict the spatial distribution of soil salinity and sodicity as a function of electrical conductivity (EC) and the exchangeable Na percentage (ESP) in agricultural lands.Three hundred soil samples were collected randomly in the study area to determine in a laboratory the EC and the ESP.The reflectance of multispectral images obtained with a camera transported in an unmanned aerial vehicle was used to calculate several reflectance indices which were used to predict EC and ESP categories.Five automated learning algorithms: Neural Networks, Random Forest, Support Vector Machine, Nearest Neighbors, and Decision Trees were trained and validated.The results of the Random Forest model indicated an accuracy of 0.69, recall of 0.69, F1 of 0.69, and kappa of 0.39 to predict EC categories.The Support vector machine was the most satisfactory model for predicting the ESP, obtaining an accuracy of 0.73, recall of 0.65, F1 of 0.69, and kappa of 0.41.We recommend monitoring the study area at different year times, by identifying and mapping areas with salinity problems, farmers could implement more effective management practices, which could increase crop productivity.

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