Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

Abstract

The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options to study plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants' signals to learn information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose Zamioculcas zamiifolia and Solanum lycopersicum (tomato) to four different stimuli: wind, heat, red and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning for the classification problem of determining the correct stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to drive a phytosensing approach for air pollution monitoring in urban areas.

Design of a Tomato Sorting Device Based on the Multisine-FSR Composite Measurement

“…This work improves the Multisine synthesis algorithm based on phase iteration optimization by Yang et al [25] and applies it to impedance measurement on tomatoes. In this work, the harmonic frequencies were 2, 3,5,7,11,13,17,19,29,37,53,73,101,139,193,269,373,521, 719, and 997 kHz, respectively.…”

“…The electrical impedance detection method is an online real-time detection method. It can not only analyze internal factors such as tomato maturity [15] or nitrogen content [14,16,17] but also theoretically determine whether there are diseases and pests inside tomatoes through experimental data, further enhancing the level of tomato sorting [14].…”

Animal–robot interaction—an emerging field at the intersection of biology and robotics

“…An ORIS focusing on plants has been proposed by Buss et al [13]. The authors investigated phytosensing in living plants through the utilization of electrical potential and tissue impedance measurements, to infer information about the surrounding environment, and to be implemented in the future for urban air pollution monitoring.…”

Diálogo Elétrico em Plantas: Técnicas de Aprendizado de Máquina Aplicadas à Identificação de Estresse

“…Para avaliar de maneira mais precisa o comportamento dos dados, é possível que os conjuntos sejam particionados em janelas no formato 𝑀 × 𝑁 , em que 𝑀 corresponde a quantidade de subconjuntos e 𝑁 o volume de amostras em cada um [8,9]. Por sua vez, as características envolvidas em sinais biológicos são geralmente avaliadas através de medidas estatísticas de dispersão, como média, variância, curtose e intervalo interquartil [9].…”