Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals

Qin, Xiao-Huang; Wang, Zi-Yang; Yao, Jie-Peng; Zhou, Qiao; Zhao, Pengfei; Wang, Zhongyi; Huang, Lan

doi:10.1016/j.compag.2020.105464

Cited by 47 publications

(29 citation statements)

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“…Although sparse, current literature asserts automated recognition of signal pattern changes in the plant electrophysiological response induced by different stressors. Either by inspecting local features or by analyzing the shape of the entire recorded waveform, the proposed approaches, which employ binary classification algorithms, provide accurate identification of the plant stressed state caused by the applied stimuli [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…The major parts of the analyzed stimuli are abiotic stressors, such as the environmental stressors of light, cold and osmotic stress [10]; the pollution stressors sodium chloride, sulphuric acid and ozone [11,12]; drought [15]; as well as salt tolerance [13], applied either on soybeans, tomato, cucumber, cabbage or wheat. However, the analyses for identifying electrophysiological alteration caused by a biotic stress at whole-plant level emerged only with a very recent study investigating the electrical signaling response of tomato plants to a pathogenic fungus [14].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Plant Electrophysiology Signals for Detection of Spider Mites Infestation in Tomatoes

et al. 2021

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Herbivorous arthropods, such as spider mites, are one of the major causes of annual crop losses. They are usually hard to spot before a severe infestation takes place. When feeding, these insects cause external perturbation that triggers changes in the underlying physiological process of a plant, which are expressed by a generation of distinct variations of electrical potential. Therefore, plant electrophysiology data portray information of the plant state. Analyses involving machine learning techniques applied to plant electrical response triggered by spider mite infestation have not been previously reported. This study investigates plant electrophysiological signals recorded from 12 commercial tomatoes plants contaminated with spider mites and proposes a workflow based on Gradient Boosted Tree algorithm for an automated differentiation of the plant’s normal state from the stressed state caused by infestation. The classification model built using the signal samples recorded during daylight and employing a reduced feature subset performs with an accuracy of 80% in identifying the plant’s stressed state. Furthermore, the Hjorth complexity encloses the most relevant information for discrimination of the plant status. The obtained findings open novel access towards automated detection of insect infestation in greenhouse crops and, consequently, more optimal prevention and treatment approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Plant Electrophysiology Signals for Detection of Spider Mites Infestation in Tomatoes

et al. 2021

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“…Our results showed ( Section 3.1 and Section 3.2 ) that increasing the stochastic heterogeneity between H + -ATP-ase activities in different plant cells, which were electrically connected, decreased the threshold of AP generation. It is known that the electrical activity of plants [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] or changes in plant reflectance caused by this activity [ 40 , 41 , 71 , 72 ] can be used for revealing actions of stressors and following physiological changes in plants. Considering the points discussed, we hypothesized that standard deviations of membrane potentials before the action potential generation could be related to the thresholds of generation of the cooling-induced APs.…”

Section: Resultsmentioning

confidence: 99%

“…It is known that the electrical activity of plants [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] or changes in plant reflectance caused by this activity [ 40 , 41 , 71 , 72 ] can be used for revealing actions of stressors and following physiological changes in plants. Considering that point, our results additionally show ( Figure 9 ) that standard deviation between averaged membrane potentials before the first AP induction (the membrane potentials at rest or the potentials under weak actions of stressors) can be potentially used for the estimation of plant electrical responses on actions of stressors and probably their adaptations to these actions.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Spatial Heterogeneity in Activities of H+-ATP-Ases in Electrically Connected Plant Cells Decreases Threshold for Cooling-Induced Electrical Responses

Sukhova

Ratnitsyna

Sukhov

2021

IJMS

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H+-ATP-ases, which support proton efflux through the plasma membrane, are key molecular transporters for electrogenesis in cells of higher plants. Initial activities of the transporters can influence the thresholds of generation of electrical responses induced by stressors and modify other parameters of these responses. Previously, it was theoretically shown that the stochastic heterogeneity of individual cell thresholds for electrical responses in a system of electrically connected neuronal cells can decrease the total threshold of the system (“diversity-induced resonance”, DIR). In the current work, we tested a hypothesis about decreasing the thresholds of generation of cooling-induced electrical responses in a system of electrically connected plant cells with increasing stochastic spatial heterogeny in the initial activities of H+-ATP-ases in these cells. A two-dimensional model of the system of electrically connected excitable cells (simple imitation of plant leaf), which was based on a model previously developed in our works, was used for the present investigation. Simulation showed that increasing dispersion in the distribution of initial activities of H+-ATP-ases between cells decreased the thresholds of generation of cooling-induced electrical responses. In addition, the increasing weakly influenced the amplitudes of electrical responses. Additional analysis showed two different mechanisms of the revealed effect. The increasing spatial heterogeneity in activities of H+-ATP-ases induced a weak positive shift of the membrane potential at rest. The shift decreased the threshold of electrical response generation. However, the decreased threshold induced by increasing the H+-ATP-ase activity heterogeneity was also observed after the elimination of the positive shift. The result showed that the “DIR-like” mechanism also participated in the revealed effect. Finally, we showed that the standard deviation of the membrane potentials before the induction of action potentials could be used for the estimation of thresholds of cooling-induced plant electrical responses. Thus, spatial heterogeneity in the initial activities of H+-ATP-ases can be a new regulatory mechanism influencing the generation of electrical responses in plants under actions of stressors.

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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%

Influence of Local Burning on Difference Reflectance Indices Based on 400–700 nm Wavelengths in Leaves of Pea Seedlings

Sukhova

Yudina

Gromova

et al. 2021

Plants

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Local damage (e.g., burning) induces a variation potential (VP), which is an important electrical signal in higher plants. A VP propagates into undamaged parts of the plant and influences numerous physiological processes, including photosynthesis. Rapidly increasing plant tolerance to stressors is likely to be a result of the physiological changes. Thus, developing methods of revealing VP-induced physiological changes can be used for the remote sensing of plant systemic responses to local damage. Previously, we showed that burning-induced VP influenced a photochemical reflectance index in pea leaves, but the influence of the electrical signals on other reflectance indices was not investigated. In this study, we performed a complex analysis of the influence of VP induction by local burning on difference reflectance indices based on 400–700 nm wavelengths in leaves of pea seedlings. Heat maps of the significance of local burning-induced changes in the reflectance indices and their correlations with photosynthetic parameters were constructed. Large spectral regions with significant changes in these indices after VP induction were revealed. Most changes were strongly correlated to photosynthetic parameters. Some indices, which can be potentially effective for revealing local burning-induced photosynthetic changes, are separately shown. Our results show that difference reflectance indices based on 400–700 nm wavelengths can potentially be used for the remote sensing of plant systemic responses induced by local damages and subsequent propagation of VPs.

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Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals

Cited by 47 publications

References 45 publications

Classification of Plant Electrophysiology Signals for Detection of Spider Mites Infestation in Tomatoes

Classification of Plant Electrophysiology Signals for Detection of Spider Mites Infestation in Tomatoes

Stochastic Spatial Heterogeneity in Activities of H+-ATP-Ases in Electrically Connected Plant Cells Decreases Threshold for Cooling-Induced Electrical Responses

Influence of Local Burning on Difference Reflectance Indices Based on 400–700 nm Wavelengths in Leaves of Pea Seedlings

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