Machine Learning -Based Macronutrient Sensing in Embedded Systems: A Review

The integration of machine learning (ML) into microcontroller-class hardware for macronutrient sensing has shown increasing potential for enhancing environmental and agricultural monitoring. This systematic review synthesizes current trends, methodologies, and outcomes in this emerging field. A PRISMA-guided review was conducted across Google Scholar, Web of Science, and Scopus, yielding 2,546 initial records. After rigorous screening, 39 studies were selected based on relevance to ML-based macronutrient detection using microcontrollers. Publication types, sensor targets, hardware-software configurations, and validation metrics were analyzed. Publication peaked in 2021 (n = 10) with journal articles comprising 82% of studies. Google Scholar contributed 74% of sources. Research was geographically diverse, with Australia leading (33%), followed by Finland (10%) and several Asian and African countries. Studies predominantly targeted subsurface (31%) and agricultural water (18%), with pH, nitrate, and phosphate as common analytes. Nutrient concentration detection showed bias toward trace levels: 93% of nitrate studies used very low values (0.03–3.1 ppm); 92% of phosphorus studies focused on values ≤ 0.7 ppm. Potassium sensing emphasized high ranges (85%), while calcium reporting was more balanced (74% in moderate ranges). Magnesium and sulfur were minimally represented, with most studies focusing on low or moderate values (95%). Arduino platforms dominated (59%) and were mostly tied to microcontroller use (67%). Bluetooth (64%) was the most employed communication protocol, favoring low-power, short-range deployment. Cloud integration was common via AWS (33%) and ThingSpeak (28%), with 36% using open-source or custom solutions. Development tools were led by Arduino IDE (59%), while advanced AI integration was limited (~ 5%). Validation metrics favored R² (49%), followed by accuracy (21%), RMSE, and MAE. ML models (KNN, RF, DT) were occasionally used for model validation but often lacked consistent metric reporting. Embedded ML sensing for macronutrient detection is a fast-evolving multidisciplinary field. While nitrate and phosphate detection is well studied, potassium, magnesium, and sulfur remain underexplored. Gaps in reporting standards and methodological transparency hinder reproducibility. Future research should address these limitations while advancing deployment in low-resource settings.