TinyML Applications in Micronutrient Sensing: A Review of Microcontroller Deployments

Deploying machine learning (ML) models on microcontroller-class hardware offers a promising pathway for real-time micronutrient sensing, especially in resource-constrained agricultural and environmental contexts. Traditional sensing methods are often cost-prohibitive and lack real-time responsiveness, while ML-embedded systems enable portable, low-power, and scalable monitoring. This systematic review investigates global research trends in applying ML on microcontroller-class hardware for micronutrient sensing. It evaluates algorithm choices, dataset characteristics, hardware specifications, performance reporting, and real-time capabilities to identify critical gaps and future opportunities. The review followed PRISMA 2020 guidelines, analyzing 43 studies published between 2015 and 2025 sourced from Scopus, Web of Science, and Google Scholar. Eligibility criteria included English-language, peer-reviewed works focusing on ML techniques for real-time micronutrient sensing using microcontroller platforms. Data were synthesized and visualized across 14 key dimensions, including model type, sensor integration, hardware constraints, and deployment scenarios. Publication activity peaked in 2022, with growing contributions from countries like Israel and Kenya. Journal articles (51.16%) and conference papers (37.21%) dominated. Most studies (46.51%) were sourced from Google Scholar. Established frameworks such as TinyML were most frequently used (39.53%), while 32.56% of studies specified exact microcontroller boards. Deep learning (37.21%) and hybrid models (20.93%) were commonly applied, often using custom datasets (39.53%). However, 46.51% of studies lacked clear model size or latency reporting. Real-time performance was confirmed in 65.12% of cases, though only 11.63% provided quantified size and latency data. Hardware constraints were often generalized (30.23%), and 16.28% of papers omitted hardware details altogether. Environmental monitoring and smart IoT applications were the most common use cases (25.58% and 13.95%, respectively), supported by domain-specific ML tools (25.58%). ML on microcontroller-class hardware shows clear potential for enabling accessible, real-time micronutrient sensing. However, reproducibility remains limited due to insufficient reporting on model performance, hardware specifics, and deployment conditions. To accelerate adoption, future work should prioritize standardization in performance reporting, interdisciplinary collaboration, and deployment in real-world field environments.