Detection of Lead Contamination Using Bioelectrical Signals of Aloe vera var. Chinensis: A Wavelet-Based and Explainable Machine Learning Approach

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.