Dataset Information

Plant neighbor detection through touching leaf tips precedes phytochrome signals.

ABSTRACT: Plants in dense vegetation compete for resources, including light, and optimize their growth based on neighbor detection cues. The best studied of such behaviors is the shade-avoidance syndrome that positions leaves in optimally lit zones of a vegetation. Although proximate vegetation is known to be sensed through a reduced ratio between red and far-red light, we show here through computational modeling and manipulative experiments that leaves of the rosette species Arabidopsis thaliana first need to move upward to generate sufficient light reflection potential for subsequent occurrence and perception of a reduced red to far-red ratio. This early hyponastic leaf growth response is not induced by known neighbor detection cues under both climate chamber and natural sunlight conditions, and we identify a unique way for plants to detect future competitors through touching of leaf tips. This signal occurs before light signals and appears to be the earliest means of above-ground plant-plant signaling in horizontally growing rosette plants.

SUBMITTER: de Wit M

PROVIDER: S-EPMC3437826 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Plant neighbor detection through touching leaf tips precedes phytochrome signals.

de Wit Mieke M Kegge Wouter W Evers Jochem B JB Vergeer-van Eijk Marleen H MH Gankema Paulien P Voesenek Laurentius A C J LA Pierik Ronald R

Proceedings of the National Academy of Sciences of the United States of America 20120820 36

Plants in dense vegetation compete for resources, including light, and optimize their growth based on neighbor detection cues. The best studied of such behaviors is the shade-avoidance syndrome that positions leaves in optimally lit zones of a vegetation. Although proximate vegetation is known to be sensed through a reduced ratio between red and far-red light, we show here through computational modeling and manipulative experiments that leaves of the rosette species Arabidopsis thaliana first ne ...[more]

PMID: 22908260

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Project description:BackgroundMechanical damage to plants triggers local and systemic electrical signals that are eventually decoded into plant defense responses. These responses are constantly affected by other environmental stimuli in nature, for instance, light fluctuation. In recent years, studies on decoding plant electrical signals powered by various machine learning models are increasing in a sense of early prediction or detection of different environmental stresses that threaten plant growth or crop yields. However, the main bottleneck is the low-throughput nature of plant electrical signals, making it challenging to obtain a substantial amount of training data. Consequently, training these models with small datasets often leads to unsatisfactory performance.ResultsIn the present work, we set out to decode wound-induced electrical signals (also termed slow wave potentials, SWPs) from plants that are deprived of light to different extents. Using non-invasive electrophysiology, we separately collected sets of local and distal SWPs from the treated plants. Then, we proposed a workflow based on few-shot learning to automatically identify SWPs. This workflow incorporates data preprocessing, feature extraction, data augmentation and classifier training. We established the integral and the first-order derivative as features for efficiently classifying SWPs. We then proposed an Adversarial Autoencoder (AAE) structure to augment the SWP samples. Combining them, the Random Forest classifier allowed remarkable classification accuracies of 0.99 for both local and systemic SWPs. In addition, in comparison to two other reported methods, our proposed AAE structure enabled better classification results using our tested features and classifiers.ConclusionsThe results of this study establish new features for efficiently classifying wound-induced electrical signals, which allow for distinguishing dark-affected local and systemic plant wound responses. We also propose a new data augmentation structure to generate virtual plant electrical signals. The methods proposed in this study could be further applied to build models for crop plants using electrical signals as inputs, and also to process other small-scale signals.

Dataset Information

Plant neighbor detection through touching leaf tips precedes phytochrome signals.

Publications

Plant neighbor detection through touching leaf tips precedes phytochrome signals.

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