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Plants
Special Issues
Remote Sensing Technologies in Crop Monitoring and Plant Phenotyping
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Remote Sensing Technologies in Crop Monitoring and Plant Phenotyping

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 427

Special Issue Editors

Dr. Qing Gu

E-Mail Website
Guest Editor
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Interests: precision agriculture; deep learning; remote sensing; plant phenotyping; breeding
Dr. Yuan Wang

E-Mail Website
Guest Editor
State Key Laboratory of Soil & Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Interests: non-destructive crop growth monitoring; smart agricultural decision-making; agricultural big data; AI in agriculture

Special Issue Information

Dear Colleagues,

Remote sensing technologies have become indispensable tools in modern agriculture and plant science, offering rapid, non-invasive, and scalable approaches to monitor crops and quantify plant traits. This Special Issue aims to highlight recent advances in remote sensing applications for crop monitoring and plant phenotyping, with a focus on both fundamental research and practical implementation. Topics of interest include UAV and satellite-based imaging, multispectral and hyperspectral sensing, LiDAR, thermal imaging, and the integration of AI for data analysis. We welcome studies that explore dynamic crop growth monitoring, stress detection, yield estimation, and trait mapping across spatial and temporal scales. Submissions may also address sensor innovation, data fusion, and phenotyping frameworks to support breeding, cultivation management, and precision agriculture. The goal is to promote interdisciplinary research that bridges remote sensing, plant biology, and agronomy to improve crop productivity and sustainability. Articles may include, but are not limited to, the following topics:

  • UAV, satellite, and ground-based imaging for crop monitoring;
  • High-throughput phenotyping using imaging technologies;
  • Remote sensing of plant growth, stress, and health status;
  • Yield prediction based on time-series remote sensing data;
  • Integration of AI and deep learning for trait extraction;
  • Multi-source data fusion for improved crop analysis;
  • Sensor development and calibration for field phenotyping;
  • Remote sensing applications in breeding and variety evaluation;
  • Disease and pest detection using remote imagery;
  • Time-series analysis of vegetation dynamics and phenological stages.

Dr. Qing Gu
Dr. Yuan Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • crop monitoring
  • plant phenotyping
  • trait analysis
  • precision agriculture
  • smart breeding

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

25 pages, 17229 KB  
Article
Improved Multi-Stage Rice Above-Ground Biomass Estimation Using Wavelet-Texture-Fused Vegetation Indices from UAV Remote Sensing
by Jinpeng Li, Qiang Cao, Shuaipeng Wang, Jiayi Li, Dongxue Zhao, Shuai Feng, Yingli Cao and Tongyu Xu
Plants 2025, 14(18), 2903; https://doi.org/10.3390/plants14182903 - 18 Sep 2025
Viewed by 306
Abstract
When estimating above-ground biomass (AGB) across multiple growth stages, vegetation indices (VIs) have limitations due to saturation under dense canopies and poor sensitivity to vertically growing organs (e.g., panicles). Discrete wavelet transform (DWT) can extract multi-directional, multi-frequency texture features reflecting canopy structure changes, [...] Read more.
When estimating above-ground biomass (AGB) across multiple growth stages, vegetation indices (VIs) have limitations due to saturation under dense canopies and poor sensitivity to vertically growing organs (e.g., panicles). Discrete wavelet transform (DWT) can extract multi-directional, multi-frequency texture features reflecting canopy structure changes, but its application in crop biomass monitoring is underexplored. Therefore, to evaluate whether DWT-based textures can be used to estimate AGB across multiple growth stages and whether combining VIs can improve estimation accuracy, two-year field experiments involving four rice varieties and five nitrogen treatments were conducted. UAV multispectral images were acquired during the critical growth stages, from which Vis and wavelet textures (WTs) were extracted, and novel wavelet texture indices (WTIs) were constructed. Correlation analysis guided feature selection, and simple regression, multiple linear regression, and Optuna-optimized random forest were employed to develop rice AGB estimation models. The results indicated: (1) Compared to a single WT, the WTIs exhibited higher correlation with rice AGB across different growth stages. (2) Among the three models, the RF model performed best. Specifically, using only VIs to estimate AGB during pre-heading yielded relatively higher accuracy (R2 = 0.713), while using WTIs to estimate AGB during post-heading and all-stage yielded higher accuracy (R2 = 0.709 and 0.668). (3) Combining WTIs with VIs significantly improves the prediction accuracy of AGB at different growth stages (R2 = 0.782, 0.769, and 0.732; RMSE = 114.655, 161.779, and 223.654 g/m2), with R2 improving by 10–15% and RMSE decreasing by 13–17% compared to the VIs. The study demonstrates that DWT-based textures can effectively assist in the high-precision estimation of rice AGB. Moreover, integrating WTIs with VIs enables accurate and stable prediction of rice AGB under different management practices and varieties, providing an economical and efficient method for estimating rice AGB. Full article
(This article belongs to the Special Issue Remote Sensing Technologies in Crop Monitoring and Plant Phenotyping)
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