数字农科院2.0

Early detection of dark-affected plant mechanical responses using enhanced electrical signals

文献类型： 外文期刊

作者： Hongping Li;Nikou Fotouhi;Fan Liu;Hongchao Ji;Qian Wu

作者机构：

关键词： （1-1-2）Classification;Data augmentation;Environmental stress;Machine learning;Plant electrical signal

期刊名称： Plant Methods

ISSN： 1746-4811

年卷期： 2024 年 20 卷 1 期

页码：

收录情况： SCIE（2024版）

摘要： Background: Mechanical 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. Results: In 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. Conclusions: The 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.

分类号：

  • 相关文献

[1]Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography. Shuaipeng Fei,Shunfu Xiao,Qing Li,Meiyan Shu,Weiguang Zhai,Yonggui Xiao,Zhen Chen,Helong Yu,Yuntao Ma. 2023

[2]The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system. Ai Dong Wang,Rui Jie Li,Xiang Qian Feng,Zi Qiu Li,Wei Yuan Hong,Hua Xing Wu,Dan Ying Wang,Song Chen. 2025

[3]Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping. Yang, Lingbo,Huang, Ran,Huang, Jingfeng,Lin, Tao,Wang, Limin,Mijiti, Ruzemaimaiti,Wei, Pengliang,Tang, Chao,Shao, Jie,Li, Qiangzi,Du, Xin. 2021

[4]OVERSAMPLING METHOD FOR ENVIRONMENTAL MONITORING DATA AUGMENTATION IN CADMIUM -POLLUTED PADDY FIELD. Hao, Peitong,Li, Yue,Xu, Weiman,Li, Cong,Huo, Mingzhu,Zhang, Xiaoyu,An, Yi. 2022

[5]Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning. Hongkai Zhu,Jingyuan Liu,Mogens L. Andersen,Günther H.J. Peters,Marianne N. Lund. 2023

[6]High-quality images and data augmentation based on inverse projection transformation significantly improve the estimation accuracy of biomass and leaf area index. Yingpu Che,Qing Wang,Ziwen Xie,Shilin Li,Jinyu Zhu,Baoguo Li,Yuntao Ma. 2023

[7]Rep-ViG-Apple: A CNN-GCN Hybrid Model for Apple Detection in Complex Orchard Environments. Bo Han,Ziao Lu,Jingjing Zhang,Rolla Almodfer,Zhengting Wang,Wei Sun,Luan Dong. 2024

[8]Efficient Triple Attention and AttentionMix: A Novel Network for Fine-Grained Crop Disease Classification. Yanqi Zhang,Ning Zhang,Jingbo Zhu,Tan Sun,Xiujuan Chai,Wei Dong. 2025

[9]GAN-MAML strategy for biomass energy production: Overcoming small dataset limitations. Yi Zhang,Yanji Hao,Yu Fu,Yijing Feng,Yeqing Li,Xiaonan Wang,Junting Pan,Yongming Han,Chunming Xu. 2025

[10]Multi-Indicator Prediction of Kiwifruit Quality integrating Hyperspectral Analysis and WGAN-GP-MMoE Framework. 刘子涵,陈谦,李佳利,刘浩松,帅博宇,欧阳凌欢,卞子晗,王君怡,于家斌,钱建平. 2025

[11]Cotton Yield Prediction with Gaussian Distribution Sampling and Variational AutoEncoder. Yaqi Lan,Xiudong Wang,Lei Gao,Xiaoliang Chen. 2025

[12]The mRNA Expression Profiles of Five Heat Shock Protein Genes from Frankliniella occidentalis at Different Stages and Their Responses to Temperatures and Insecticides. Wang Li-xia,Li Xue,Lei Zhong-ren,Reitz, Stuart R.,Wang Shuai-yu. 2014

[13]Genetic analysis and QTL detection of reproductive period and post-flowering photoperiod responses in soybean. Cheng, Lirui,Wang, Ying,Zhang, Chunbin,Wu, Cunxiang,Xu, Jianlong,Zhu, Huiying,Leng, Jiantian,Bai, Yangnian,Guan, Rongxia,Hou, Wensheng,Han, Tianfu,Cheng, Lirui,Zhang, Lijuan.

[14]Drought-induced site-specific DNA methylation and its association with drought tolerance in rice (Oryza sativa L.). Wang, Wen-Sheng,Pan, Ya-Jiao,Zhao, Xiu-Qin,Zhu, Ling-Hua,Fu, Bin-Ying,Li, Zhi-Kang,Wang, Wen-Sheng,Dwivedi, D.,Ali, J.,Li, Zhi-Kang.

[15]Regulation of plant stress response by dehydration responsive element binding (DREB) transcription factors. Zhou, Mei-Liang,Ma, Jiang-Tao,Pang, Jun-Feng,Tang, Yi-Xiong,Wu, Yan-Min,Ma, Jiang-Tao,Pang, Jun-Feng,Zhang, Zhan-Lu,Zhou, Mei-Liang. 2010

[16]Promotive effects of 5-aminolevulinic acid on photosynthesis and chlorophyll fluorescence of tomato seedlings under suboptimal low temperature and suboptimal photon flux density stress - Short communication. Guo, Xiaoqing,Li, Yansu,Yu, Xianchang. 2012

[17]SakA Regulates Morphological Development, Ochratoxin A Biosynthesis and Pathogenicity of Aspergillus westerdijkiae and the Response to Different Environmental Stresses. Peidong Si,Gang Wang,Wenqing Wu,Sarfaraz Hussain,Ling Guo,Wei Wu,Qingli Yang,Fuguo Xing. 2023

[18]Unlocking the potentials of nitrate transporters at improving plant nitrogen use efficiency. Oluwaseun Olayemi Aluko,Surya Kant,Oluwafemi Michael Adedire,Chuanzong Li,Guang Yuan,Haobao Liu,Qian Wang. 2023

[19]The small RNA STnc1480 contributes to the regulation of biofilm formation and pathogenicity in Salmonella typhimurium. Jing Li,Chengcheng Ning,Na Li,Yun Guo,Chunhui Ji,Xiaozhen Zhu,Xingxing Zhang,Qingling Meng,Xianzhu Xia,Xuepeng Cai,Jun Qiao. 2022

[20]Quantitative responses of tomato yield, fruit quality and water use efficiency to soil salinity under different water regimes in Northwest China. Hao Li,Xuemin Hou,Nadia Bertin,Risheng Ding,Taisheng Du. 2023

作者其他论文 更多>>