Benchmarking organic electrochemical transistors for plant electrophysiology

A Roadmap from Functional Materials to Plant Health Monitoring (PHM)

Soft bioelectronics have great potential for the early diagnosis of plant diseases and the mitigation of adverse outcomes such as reduced crop yields and stunted growth. Over the past decade, bioelectronic interfaces have evolved into miniaturized conformal electronic devices that integrate flexible monitoring systems with advanced electronic functionality. This development is largely attributable to advances in materials science, and micro/nanofabrication technology. The approach uses the mechanical and electronic properties of functional materials (polymer substrates and sensing elements) to create interfaces for plant monitoring. In addition to ensuring biocompatibility, several other factors need to be considered when developing these interfaces. These include the choice of materials, fabrication techniques, precision, electrical performance, and mechanical stability. In this review, some of the benefits plants can derive from several of the materials used to develop soft bioelectronic interfaces are discussed. The article describes how they can be used to create biocompatible monitoring devices that can enhance plant growth and health. Evaluation of these devices also takes into account features that ensure their long-term durability, sensitivity, and reliability. This article concludes with a discussion of the development of reliable soft bioelectronic systems for plants, which has the potential to advance the field of bioelectronics.

Plant electrophysiology with conformable organic electronics: Deciphering the propagation of Venus flytrap action potentials

Electrical signals in plants are mediators of long-distance signaling and correlate with plant movements and responses to stress. These signals are studied with single surface electrodes that cannot resolve signal propagation and integration, thus impeding their decoding and link to function. Here, we developed a conformable multielectrode array based on organic electronics for large-scale and high-resolution plant electrophysiology. We performed precise spatiotemporal mapping of the action potential (AP) in Venus flytrap and found that the AP actively propagates through the tissue with constant speed and without strong directionality. We also found that spontaneously generated APs can originate from unstimulated hairs and that they correlate with trap movement. Last, we demonstrate that the Venus flytrap circuitry can be activated by cells other than the sensory hairs. Our work reveals key properties of the AP and establishes the capacity of organic bioelectronics for resolving electrical signaling in plants contributing to the mechanistic understanding of long-distance responses in plants.

Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data

Plant electrophysiological response contains useful signature of its environment and health which can be utilized using suitable statistical analysis for developing an inverse model to classify the stimulus applied to the plant. In this paper, we have presented a statistical analysis pipeline to tackle a multiclass environmental stimuli classification problem with unbalanced plant electrophysiological data. The objective here is to classify three different environmental chemical stimuli, using fifteen statistical features, extracted from the plant electrical signals and compare the performance of eight different classification algorithms. A comparison using reduced dimensional projection of the high dimensional features via principal component analysis (PCA) has also been presented. Since the experimental data is highly unbalanced due to varying length of the experiments, we employ a random under-sampling approach for the two majority classes to create an ensemble of confusion matrices to compare the classification performances. Along with this, three other multi-classification performance metrics commonly used for unbalanced data viz. balanced accuracy, F1-score and Matthews correlation coefficient have also been analyzed. From the stacked confusion matrices and the derived performance metrics, we choose the best feature-classifier setting in terms of the classification performances carried out in the original high dimensional vs. the reduced feature space, for this highly unbalanced multiclass problem of plant signal classification due to different chemical stress. Difference in the classification performances in the high vs. reduced dimensions are also quantified using the multivariate analysis of variance (MANOVA) hypothesis testing. Our findings have potential real-world applications in precision agriculture for exploring multiclass classification problems with highly unbalanced datasets, employing a combination of existing machine learning algorithms. This work also advances existing studies on environmental pollution level monitoring using plant electrophysiological data.