Monitoring Iron Stress in Tomato Plants Through Bioimpedance and Machine-Learning-Enhanced Classification Based on Circuit Component Analysis

Insufficient availability of essential nutrients, such as iron, can impede plant growth, decrease crop productivity, and even lead to plant death. This is why it is crucial to employ proximal monitoring techniques to detect early signs of nutrient stress and prevent yield loss. In this study, we continuously monitored the stem impedance of eight tomato plants every hour for 38 days. This was done to observe the effects of iron stress by comparing these plants with those not under stress. The normalized impedance magnitude at 10 kHz reveals a noticeable divergence in the trend of impedance magnitude shortly after the removal of iron from the nutrient solution, clearly indicating the effect of iron stress on plant bioimpedance. Additionally, the Cole equivalent circuit model was employed to evaluate the electrical parameters of the impedance spectra. The fitting results exhibit an average root-mean-square error of 466.3 $\Omega$ . Statistical analysis of the extracted circuit parameters shows significant differences between iron-stressed and control plants. Based on this hypothesis, the extracted circuit components have been used to train the machine learning classification model with several algorithms, to demonstrate that the multilayer perceptron is the best performing model, yielding 98% accuracy and 91% and 89% precision in identifying early and late stress, respectively. This research demonstrates the effectiveness of bioimpedance measurements in tracking iron stress in plants. Our findings highlight the usefulness of impedance measurements for monitoring iron stress in plants and provide insights into the physiological responses of tomato plants to nutrient deprivation by observing changes in bioimpedance circuit parameters over time.