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Remote Sensing
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High-Throughput Crop Phenotyping Using Unmanned Aerial Vehicle Imagery
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High-Throughput Crop Phenotyping Using Unmanned Aerial Vehicle Imagery

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 3241

Special Issue Editors

Dr. Bo Li

E-Mail Website
Guest Editor
Seeds Research, Syngenta, Jealott’s Hill, Warfield, Bracknell RG42 6EY, UK
Interests: computer vision; image analysis; plant phenotyping; remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jiangang Liu

E-Mail Website
Guest Editor
Department of Potato, Institute of Vegetables and Flowers Chinese Academy of Agricultural Sciences (IVF-CAAS), Beijing 100081 China
Interests: crop model; plant phenotyping; UAV; proximal remote sensing; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Guo

E-Mail Website
Guest Editor
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 188-0002, Japan
Interests: agricultural informatics; plant phenomics; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Talukder Zaki Jubery

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Iowa State University, Ames, IA 50010, USA
Interests: crop modeling; high-throughput phenotyping; micro-nano sensors; image processing; deep learning

Special Issue Information

Dear Colleagues,

The global population is expected to reach 9.6 billion by 2050, which will introduce enormous challenges to the agricultural sector, with the context of the limited availability of arable lands, scarcity of irrigation water, and severe negative impact of climate change. These challenges have encouraged the efforts in breeding programs, which investigate the genetic diversity in germplasm collection to identify the crop traits relating to the resistance of abiotic/biotic stress and crop production. Genetic tools always lead to huge amounts of data, but the extraction of phenotypic traits from large-scale and time series crop imaging data remains unsatisfactory. Consequently, bridging the gap between phenotypes and genotypes is a significant research field in modern agriculture. Unmanned Aerial Vehicles (UAVs), which can carry a range of imaging sensors, typically in the visible and infrared domain but also in both 2D and 3D formats, have been employed in high-throughput and non-destructive crop phenotyping over time. Recent developments in sensor technology, image analysis, and machine learning need to be integrated with UAV imagery to gain more quantitative knowledge of key plant traits in crop breeding and production.

This Special Issue aims to collect the results of the latest innovative research in the application of UAV imagery and machine learning for the high-throughput phenotyping. Original research articles and reviews are welcome in both agricultural and horticultural areas. The list below provides a general (but not exhaustive) overview of the topics that are solicited for this Special Issue:

  • Novel UAV imaging sensors for plant phenotyping;
  • 2D or 3D image analysis algorithms including object detection, segmentation, and classification for key crop trait estimation;
  • UAV imaging sensor calibration;
  • Sensor fusion and corresponding image analysis.

Dr. Bo Li
Dr. Jiangang Liu
Dr. Wei Guo
Dr. Talukder Zaki Jubery
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. Remote Sensing 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

  • unmanned aerial vehicle
  • imaging sensor technology
  • crop traits
  • image analysis
  • high-throughput crop phenotyping
  • machine learning
  • 3D modelling
  • spectral analysis
  • object recognition, segmentation, and classification
  • data fusion

Published Papers (2 papers)

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Research

20 pages, 7273 KiB  
Article
Prediction of Seedling Oilseed Rape Crop Phenotype by Drone-Derived Multimodal Data
by Yang Yang, Xinbei Wei, Jiang Wang, Guangsheng Zhou, Jian Wang, Zitong Jiang, Jie Zhao and Yilin Ren
Remote Sens. 2023, 15(16), 3951; https://doi.org/10.3390/rs15163951 - 09 Aug 2023
Cited by 1 | Viewed by 1244
Abstract
In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data [...] Read more.
In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R2 = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model’s robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed. Full article
(This article belongs to the Special Issue High-Throughput Crop Phenotyping Using Unmanned Aerial Vehicle Imagery)
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20 pages, 15813 KiB  
Article
Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing
by Weiguang Zhai, Changchun Li, Qian Cheng, Fan Ding and Zhen Chen
Remote Sens. 2023, 15(13), 3454; https://doi.org/10.3390/rs15133454 - 07 Jul 2023
Cited by 3 | Viewed by 1597
Abstract
Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also [...] Read more.
Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field. Full article
(This article belongs to the Special Issue High-Throughput Crop Phenotyping Using Unmanned Aerial Vehicle Imagery)
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