An Explainable Smart Agriculture System based on In- Vivo Biosensors

Some of the most significant factors regarding plant growth and food production are for sure water stress and drought. Predicting the water stress of crops in advance with respect to its visible signs is priceless and could permit one to intervene early to restore healthy growth conditions. In this paper, we discuss an Explainable Smart Agriculture System for monitoring the water stress status of tomato plants based on a novel in-vivo biosensor. Specifically, we embed, in the proposed system, an intrinsically explainable classifier, namely a fuzzy decision tree, to characterize the status of the plants in four different categories. To this aim, we extract four features related to the ionic currents inside the sap of the plants themselves. Thanks to the explainable classifier, we offer insights into the classification of the status of the plants. This contributes to a deeper understanding of the unseen processes occurring within the plants, enabling early detection of stress due to water shortage before it becomes visibly apparent. We evaluate the effectiveness of our approach considering the real data extracted from in-vivo biosensors deployed on two different types of tomato plants. Preliminary results show that the proposed explainable classifier achieves promising results in terms of both explainability and classification capability. Additionally, we present and discuss some examples of rules derived from the decision trees, emphasizing their significance in understanding the sap activities within plants. This understanding aids in implementing effective countermeasures, for example in real-world on-the-field automated irrigation systems, to maintain plant health.