The soil EC was predicted using smartphone-based (iPhone 11) color coordinates (RGB, HSV, and CIE L*a*b*) and individual and combined Vis-NIR and pXRF spectra. Prediction models used were: one-dimension (1D) datadriven machine learning and recurrent neural network (RNN), and two-dimension (2D) convolutional neural network (CNN). A total of 240 soil samples were collected from 0 to 20 cm depth and air-dried samples were used for smartphone-based digital images and spectra (Vis-NIR and pXRF). Most smartphone-based and Vis-based color coordinates could be used to predict EC and salinity classes. Combined Vis-NIR and pXRF spectra had the highest prediction accuracy (R2 = 0.93) for predicting EC compared to individual Vis-NIR or pXRF spectra and smartphone-based and Vis-based color coordinates. We conclude that smartphone-based digital images based on the 2D-CNN model can be used to predict EC, but we recommend using combined Vis-NIR + pXRF spectra with gated recurrent unit (GRU) model for the highest prediction accuracy.