Electrical response of plants to environmental stimuli: A short review and perspectives for meteorological applications

Signaling and transport processes related to the carnivorous lifestyle of plants living on nutrient-poor soil

In Eukaryotes, long-distance and rapid signal transmission is required in order to be able to react fast and flexibly to external stimuli. This long-distance signal transmission cannot take place by diffusion of signal molecules from the site of perception to the target tissue, as their speed is insufficient. Therefore, for adequate stimulus transmission, plants as well as animals make use of electrical signal transmission, as this can quickly cover long distances. This update summarises the most important advances in plant electrical signal transduction with a focus on the carnivorous Venus flytrap. It highlights the different types of electrical signals, examines their underlying ion fluxes and summarises the carnivorous processes downstream of the electrical signals.

Machine Learning for Automatic Classification of Tomato Ripening Stages Using Electrophysiological Recordings

The physiological processes underlying fruit ripening can lead to different electrical signatures at each ripening stage, making it possible to classify tomato fruit through the analysis of electrical signals. Here, the electrical activity of tomato fruit (Solanum lycopersicum var. cerasiforme) during ripening was investigated as tissue voltage variations, and Machine Learning (ML) techniques were used for the classification of different ripening stages. Tomato fruit was harvested at the mature green stage and placed in a Faraday's cage under laboratory-controlled conditions. Two electrodes per fruit were inserted 1 cm apart from each other. The measures were carried out continuously until the entire fruits reached the light red stage. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. Descriptive analysis from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis). Finally, ApEn, PCA1, PCA2, and PCA3 were obtained. These features were used in ML analyses for looking for classifiable patterns of the three different ripening stages: mature green, breaker, and light red. The results showed that it is possible to classify the ripening stages using the fruit's electrical activity. It was also observed, using precision, sensitivity, and F1-score techniques, that the breaker stage was the most classifiable among all stages. It was found a more accurate distinction between mature green × breaker than between breaker × light red. The ML techniques used seem to be a novel tool for classifying ripening stages. The features obtained from electrophysiological time series have the potential to be used for supervised training, being able to help in more accurate classification of fruit ripening stages.