Heavy metal contamination, particularly lead (Pb), represents a threat to ecosystems and human health. This study investigates the variety Aloe vera var. Chinensis as a plant sensing platform for detecting the presence of lead by characterizing its bioelectrical response. A low-cost system based on Arduino was developed to acquire real-time electrical signals from 160 plants, equally divided between two groups: control conditions (n = 80) and Pb acetate exposure (500 mg/L; n = 80). Two recording sessions per plant were obtained after the plant had stabilized, resulting in 320 labeled measurements. The signals were characterized using the discrete wavelet transform (DWT), autoregressive (AR) models, and complexity measures based on entropy. Three classifiers—Support Vector Machine, Random Forest, and XGBoost—were trained and evaluated using five-fold cross-validation and a held-out test set with plant disjoint samples. XGBoost achieved the highest performance (accuracy = 93.0%; precision = 92.5%; recall = 93.8%; F1-score = 93.1%; and 95% CI for accuracy: 90.4–95.2% via bootstrap), significantly outperforming the other models. SHAP analysis revealed that midscale wavelet entropy and energy features, along with AR residual variance, were the most discriminative for Pb detection. These findings demonstrate a scalable, low-cost, and interpretable biosensing framework with potential applications in real-time environmental monitoring and early detection of heavy metal contamination.

Recent advances in edge computing and Tiny Machine Learning (TinyML) have enabled the deployment of artificial intelligence models directly on microcontrollers with extremely limited computational and memory resources. In this context, this work presents the design, implementation, and validation of a real-time cardiac arrhythmia classification system based on a quantized one-dimensional convolutional neural network (1D-CNN), deployed on an 8-bit Arduino UNO microcontroller. The proposed system integrates end-to-end processing, including ECG signal acquisition using a low-cost AD8232 analog front-end, signal preprocessing, heartbeat segmentation, classification, and real-time visualization on an OLED display. The model was trained and evaluated using the MIT-BIH Arrhythmia Database, considering a reduced three-class problem (Normal, Ventricular, and Supraventricular) to meet the constraints of ultra-low-cost hardware deployment. Under benchmark conditions, the quantized model achieved an accuracy of 97.6%, with a memory footprint below 24 KB and an average inference time of 200 ms per heartbeat, enabling real-time operation on a resource-constrained microcontroller. Real-time experiments were conducted using signals acquired from healthy volunteers to validate system functionality, although no annotated ground truth was available for these recordings, and therefore no diagnostic performance was derived from them. The results demonstrate the feasibility of deploying lightweight deep learning models on ultra-constrained embedded systems using the TinyML paradigm, implemented using TensorFlow 2.15 and TensorFlow Lite. This work should be interpreted as a proof-of-concept platform that highlights the trade-off between classification performance and hardware limitations, providing a foundation for future development of low-cost cardiac monitoring technologies in resource-limited environments.

Environmental monitoring across extensive regions is often constrained by the high costs of conventional laboratory analysis. This study proposes a methodology for electrophysiological characterization of Aloe vera as a potential biological dosimeter for low-cost environmental sensing. Using an ATMega328P-based acquisition system with high-input-impedance signal conditioning, we recorded plant biopotentials under controlled abiotic stressors. Signal variations were evaluated as a function of leaf morphology, electrode placement, and environmental variables, including light intensity, soil moisture, water saturation, and pH. The statistical validation included Jaccard similarity coefficients for repeatability and Kruskal–Wallis tests for group comparisons. The measurements showed highly repeatable baseline behavior (Jaccard similarity in the range 0.95–0.99) and significant differences across stress conditions, particularly under changes in light intensity. These findings support the feasibility of using Aloe vera electrophysiological signals as a robust and low-cost basis for developing plant-based biosensing approaches in environmental monitoring applications.