Improved Natural Plant Electrophysiology Sensor Design for Phytosensing System

In recent years, plant electrophysiology has become a viable measurement tool for describing plants' thinking properties. In this article, we propose a new electrophysiological sensor as an autonomous device, which can transfer a plant's potential electrical data into self-awareness information (a.k.a, phytosensing). Also, this device holds the potential to explore the electrophysiology of plants and its utilization in plant factories, along with its integration with intelligent monitoring and artificial intelligence (AI) technology to evaluate a plant's cognitive capabilities. Plant sensing can measure certain aspects of a plant's natural internal state. The proposed sensor circuit design is based on two-stage adjustable processing layers in electrophysiology. The major contribution of this article is to propose an electrophysiological sensor module that provides plant thinking data concerning environmental factors with dedicated surrounding conditions. Furthermore, the collected data were analyzed through a K-nearest neighbor (KNN) algorithm, which provides a feasible and less complex way to implement the supervised machine learning algorithm. The experimental result shows that each dedicated sensing condition, such as light, heat, humidity, and CO2, with the external stimuli through the proposed plant's electrophysiology sensor module, can evaluate a plant's thinking behavior. The proposed sensor module has a wide range of gains in circuits with less required complexity. Furthermore, it is suitable for larger scale data measurement for multiple plant nodes up to 16 channels.