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<p>The aim of this study was to evaluate the effect of pre-germination treatments (magneto-priming and immersion of seeds in gibberellic acid solution) on variables associated with germination, emergence and vigor of Passiflora edulis seeds ‘BRS Gigante Amarelo’ cultivar. Seeds were extracted from fruits, washed, immersed for 6 hours in solutions with different GA3 concentrations and later arranged in a circular form in Petri dishes at temperature of 25°C, with and without exposure to magnetic field. Subsequently, analyses associated with the germination and emergency test were carried out. The experimental design was completely randomized design, with 3x2 factorial, three GA3 concentrations (0, 50 and 100 mg L-1) and presence/absence of magnetic field (MF), with four replicates of 20 seeds each. Variables germination percentage, germination speed index, mean germination time, percentage of emerged seedlings, emergence speed index, shoot length and root length and seedling dry weight were evaluated. Results indicate that the exposure of passion fruit seeds to MF in an isolated way stimulates seed germination, emergence and vigor, being an alternative to conventional treatments based on chemical substances.</p>

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.

To survive in a dynamic environment growing fixed to the ground, plants have developed mechanisms for monitoring and perceiving the environment. When a stimulus is perceived, a series of signals are induced and can propagate away from the stimulated site. Three distinct types of systemic signaling exist, i.e., (i) electrical, (ii) hydraulic, and (iii) chemical, which differ not only in their nature but also in their propagation speed. Naturally, plants suffer influences from two or more stimuli (biotic and/or abiotic). Stimuli combination can promote the activation of new signaling mechanisms that are explicitly activated, as well as the emergence of a new response. This study evaluated the behavior of electrical (electrome) and hydraulic signals after applying simple and combined stimuli in common bean plants. We used simple and mixed stimuli applications to identify biochemical responses and extract information from the electrical and hydraulic patterns. Time series analysis, comparing the conditions before and after the stimuli and the oxidative responses at local and systemic levels, detected changes in electrome and hydraulic signal profiles. Changes in electrome are different between types of stimulation, including their combination, and systemic changes in hydraulic and oxidative dynamics accompany these electrical signals.

Fruits, like other parts of the plant, appear to have a rich electrical activity that may contain information. Here, we present data showing differences in the electrome complexity of tomato fruits through ripening and discuss possible physiological processes involved. The complexity of the signals, measured through approximate entropy, varied along the fruit ripening process. When analyzing the fruits individually, a decrease in entropy values was observed when they entered the breaker stage, followed by a tendency to increase again when they entered the light red stage. Consequently, the data obtained showed a decrease in signal complexity in the breaker stage, probably due to some physiological process that ends up predominating to the detriment of others. This result may be linked to processes involved in ripening, such as climacteric. Electrophysiological studies in the reproductive stage of the plant are still scarce, and research in this direction is of paramount importance to understand whether the electrical signals observed can transmit information from reproductive structures to other modules of plants. This work opens the possibility of studying the relationship between the electrical activity and fruit ripening through the analysis of approximate entropy. More studies are necessary to understand whether there is a correlation or a cause-response relationship in the phenomena involved. There is a myriad of possibilities for the applicability of this knowledge to different areas, from understanding the cognitive processes of plants to achieving more accurate and sustainable agriculture.