SOIL PHOSPHORUS TEST USING A LOW-COST SPECTROPHOTOMETER AND MACHINE LEARNING

ABSTRACT

Phosphorus concentration is one of the main attributes determined in laboratory analyses of soil samples collected in the field. The objective is to develop a soil phosphorus test using a low-cost spectrophotometer and a machine learning technique. For reflectance measurements, a low-cost system consisting of a Sparkfun AS7625x spectrophotometer and an Arduino Uno is used. Ion exchange resins under standard saturated solutions and modified conditions are used to extract phosphorus ions from the soil samples. Reflectance and phosphorus concentrations determined by the reference method are used in the training and testing of a machine learning. A modification procedure of the ion-exchange resin saturation solution allows the establishment of a strong correlation between the reflectance in 18 spectral bands and P concentration of the soil samples. The obtained model uses five reflectance of the modified resins at wavelengths of 410, 460, 560, 705, and 645 nm to predict the phosphorus concentration. This model presents an R²t accuracy of 0.97 in the training stage with an R²v of 0.96, RMSEv of 9.05, and ratio of prediction to deviation) of 3.81 in the test step.

precision agriculture; proximal spectroscopy sensors; embedded systems; ion exchange resins; machine learning by multiple linear regression