Evidence map›Paper›PMID 41354977›Full record

ArticlePlant methods2025

A wireless leaf movement sensor system for early detection of abiotic stresses in Zea mays L.

Xu Zhang, Xiang Li, Ming Li, Yumeng Li, Chunying Wang, Haixia Yu, Shidong He, Tingting Zhai, Ping Liu

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In one paragraph

Article in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Xu Zhang *College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, 271018, China.
Xiang Li *State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai'an, 271018, China. lixiang@sdau.edu.cn.
Ming LiCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, 271018, China.
Yumeng LiCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, 271018, China.
Chunying WangCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, 271018, China.
Haixia YuState Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai'an, 271018, China.
Shidong HeState Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai'an, 271018, China.
Tingting ZhaiCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, 271018, China.
Ping LiuCollege of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, 271018, China. liuping@sdau.edu.cn.

Funding

Key Technology Research and Development Program of Shandong Province 2022LZGCQY002Natural Science Foundation of Shandong Province ZR2020KF002NSFC 31871543
6 · The paper itself

Abstract

backgroundAbiotic stresses are detrimental factors for germination, organ development, and other growth activities in maize, which could reduce yield and quality. The analysis of leaf movement is a simple and efficient method to identify stresses as early as possible. This study developed a wireless leaf movement sensor system (WLMS) using a digital inertial measurement unit (IMU) to measure maize leaf movement in real-time and detect abiotic stresses quickly.

resultsThe IMU was designed as a lightweight sensor structure that the IMU was separated from the MCU (microcontroller unit) and connected via flexible cables. This lightweight sensor attached to maize leaves easily and measured leaf movement in real-time with high resolution (measured error of ± 0.25°). The IMU collected leaf movement data and transmitted the data wirelessly to the data receiving terminal (host computer). Meanwhile, the data receiving terminal performed linear fitting on the daily leaf movement data to extract the movement characteristics of maize leaves. The WLMS detected abiotic stress in maize based on the leaf movement characteristics under different stress conditions. The results indicated that the WLMS could detect whether maize was under stress within one day of being stressed and identify the specific type of stress within the following 5-7 days, providing a lead time of 2 days compared to other non-destructive methods (including RGB imaging, hyperspectral analysis, and chlorophyll meters).

conclusionsThis sensor system enables the rapid and early detection and identification of abiotic stresses in maize as a low-cost tool for plant phenotype measurement and plant movement measurement.

Indexed as

Abiotic stressLeaf movementLeaf sensor systemLeaf trackingPrecision agricultureStress detection

Identifiers

PMID41354977
PMCPMC12701594

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