Accuracy of Remote XY Android App in Monitoring Compost NPK and Humidity Levels using HMI

Abstract

This study evaluates the accuracy of the Remote XY application for monitoring compost parameters, using a Human–Machine Interface (HMI) as the reference standard, as it is directly connected to the compost being measured. Ten compost samples were tested, with readings recorded initially and remeasured after 24 hours under similar environmental conditions. Consistency between initial and repeated measurements was assessed using the Pearson Correlation Coefficient, while the Coefficient of Variation (CV) quantified relative variability in Remote XY readings. Results showed strong correlations for Nitrogen (r = 0.97), Phosphorus (r = 0.98), and Humidity (r = 0.97), indicating high reliability, with Potassium displaying a slightly lower yet strong correlation (r = 0.85). CV values ranged from the lowest in Humidity (22.04%) to the highest in Nitrogen (59.20%), reflecting relative stability or variability across parameters. These findings suggest that Remote XY provides readings largely consistent with the HMI, although Potassium measurements exhibited comparatively greater variation. The high correlation in key nutrient and humidity data supports the potential of Remote XY as a viable tool for continuous compost monitoring, enhancing process management and decision-making. Refinement in potassium detection could further improve overall accuracy, contributing to the optimization of IoT-based composting systems and enabling more efficient, datadriven waste-to-fertilizer processes