Early-Stage Research to Characterize the Electrical Signal of Optically Stimulated Hydroponic Strawberries Using Machine Learning Techniques

Through the electrical signal generated by a plant, it is possible to identify water stress, pests on its roots, a sick plant, or even identify the optimal growing conditions. In particular, the optimal growing conditions in the strawberry plant can be identified by its electrical signal, which can be useful to increase its production, since its fruits are in high demand for human consumption. Therefore, the aim of this pilot study is to use machine learning techniques to characterize the electrical signal of optically stimulated hydroponic strawberries, in order to identify the optimal growing conditions. The electrical signal was monitored using a home-made electronic system, based on Arduino. The principal result obtained shows that red light is the most informative feature in the random forest (RF) model, demonstrating superior performance in minimizing misclassification rates. In contrast, the support vector machine (SVM) model exhibited increased sensitivity to data variations, resulting in elevated misclassification rates. The feature importance analysis shows that the variable red light contributes 35% to the predictive capability of the model. Natural light and green light follow with approximately 25% each, while the contribution of yellow light is negligible at 15%. Finally, in this exploratory study, it would appear that the electrical signal from the plant is sensitive to specific light conditions, with red light being most impactful.