Leveraging Incremental Decision Trees and In-Vivo Biosensors for an Explainable Plant Health Monitoring System

Among the factors concerning plant development and agricultural yield, water stress and drought emerge as pivotal factors. Indeed, the ability to know in advance imminent water stress in crops based on measurable biochemical metrics is priceless, as it offers the opportunity for rapid interventions aimed at restoring optimal growth conditions before the plants show clear visible stress symptoms.In this work, we present an explainable system for smart agriculture focused on the continuous monitoring of the water stress condition of tomato plants, achieved through a new in-vivo biosensor, named bioristor. The proposed system embeds an incremental and explainable by design classifier. Specifically, we experimented with the traditional Hoeffding decision tree and its fuzzy version. This system analyzes the data received from bioristors to assess the health status of a tomato plant and classifies it into four classes. The proposed system also leverages an incremental learning technique, which allows the classification model to be updated during the monitoring period, to maintain adequate classification performance. In this way, the conditions of the plants are monitored continuously with an effective model, allowing for timely countermeasures to be taken if a water stress situation is detected. We present preliminary results on a real dataset, using four features related to the ionic currents within the plant sap, measured through bioristors. We assessed the system performance both in terms of classification ability and model complexity, obtaining promising results and the generation of interesting rules that could allow the implementation of effective countermeasures to keep the plants healthy as long as possible.