Work

Research on maize leaves surface action potential recognition method based on ResNet-18SE

Year: 2025

Type: article

Abstract: Plant action potentials are rapid changes in cellular electrical potentials, serving as critical indicators of plant physiological activity. This study introduces a novel maize leaf surface action potential recognition method by leveraging the ResNet-18SE model combined with Short-Time Fourier Transform (STFT) to convert one-dimensional electrophysiological signals into two-dimensional time-frequency domain images. The dataset was enhanced using advanced techniques, including random cropping, transformations, and generative adversarial networks (GANs). The ResNet-18SE model, integrated with Squeeze-and-Excitation (SE) blocks, achieved superior classification accuracy (95.60%), precision (96.80%), and recall (98.90%), surpassing VGG16, ResNeXt, SqueezeNet, and AlexNet. These results highlight the robustness and efficiency of combining deep learning with frequency-domain analysis in plant electrophysiological signal processing, providing a scalable framework for real-time plant health monitoring and advancing the study of plant physiology.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.100819

Authors: Zengqiang Ren, Fangming Tian, Siqi Wang, Shuqiang Chen

Institutions: Heilongjiang Bayi Agricultural University, Rice Research Institute, Heilongjiang Academy of Agricultural Machinery Engineering

Language: English

Topic: Plant and Biological Electrophysiology Studies

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,770