2021
DOI: 10.3389/fpls.2021.594195
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Detection of Different Hosts From a Distance Alters the Behaviour and Bioelectrical Activity of Cuscuta racemosa

Abstract: In our study, we investigated some physiological and ecological aspects of the life of Cuscuta racemosa Mart. (Convolvulaceae) plants with the hypothesis that they recognise different hosts at a distance from them, and they change their survival strategy depending on what they detect. We also hypothesised that, as an attempt of prolonging their survival through photosynthesis, the synthesis of chlorophylls (a phenomenon not completely explained in these parasitic plants) would be increased if the plants don’t … Show more

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Cited by 26 publications
(38 citation statements)
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References 102 publications
(188 reference statements)
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“…For the generation of the features to feed machine learning (ML) algorithms, the Fast Fourier Transform (FFT), Spectral Power Density (PSD), Wavelets, and Approximate Entropy (ApEn) were calculated for each of the samples (Saraiva et al, 2017;Parise et al, 2021). For FFT, PSD, and Wavelets we calculate the average, the maximum value, the minimum value, the variance, skewness, and kurtosis.…”
Section: Machine Learning Classification Methods On Electrome Time Seriesmentioning
confidence: 99%
See 3 more Smart Citations
“…For the generation of the features to feed machine learning (ML) algorithms, the Fast Fourier Transform (FFT), Spectral Power Density (PSD), Wavelets, and Approximate Entropy (ApEn) were calculated for each of the samples (Saraiva et al, 2017;Parise et al, 2021). For FFT, PSD, and Wavelets we calculate the average, the maximum value, the minimum value, the variance, skewness, and kurtosis.…”
Section: Machine Learning Classification Methods On Electrome Time Seriesmentioning
confidence: 99%
“…For FFT, PSD, and Wavelets we calculate the average, the maximum value, the minimum value, the variance, skewness, and kurtosis. Moreover, in order to decrease the computational cost speeding the analysis, we decided to reduce these features by calculating a Principal Component Analysis (PCA) in the descriptive analysis calculated for FFT, PSD, and Wavelets (Parise et al, 2021). Thus, we obtained the first three PCA components (PCA1, PCA2, and PCA3).…”
Section: Machine Learning Classification Methods On Electrome Time Seriesmentioning
confidence: 99%
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“…It can be expected that monitoring of electrical activity and ES-induced physiological changes is a potential tool for revealing the actions of stressors on plants. Investigations of plant electrical activity show that (i) the total electrical activity of plants (“electrome”) can be strongly dependent on abiotic and biotic factors [ 53 , 54 , 55 , 56 , 57 ], and (ii) analysis of the electrical activity can be used for the classification of stressors that act on plants [ 58 , 59 , 60 , 61 , 62 ]. However, direct measurements of electrical activity cannot be used for the remote sensing of ES-induced systemic responses because electrodes would need to be connected to the plant.…”
Section: Introductionmentioning
confidence: 99%