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Remote Sensing for Crop Growth Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 654

Special Issue Editors


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Guest Editor
Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: ultrasound propagation in complex media; ultrasonic metamaterials; ultrasound focusing; ultrasonic lenses
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Crop Production Department, Universitat Politècnica de València, Cno Vera 14, 46020 Valencia, Spain
Interests: agronomy and remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have observed growing interest in the application of remote sensing in agriculture, offering novel opportunities to enhance crop production and yields and reduce environmental impact. Thanks to remote sensing, it is possible to monitor the phenological state of crops and detect pests and diseases, water requirements, and other factors that determine their production. However, we still need to work on analyzing and interpreting remote sensing data so that they can be used more effectively.

Therefore, this Special Issue aims to combine original research and review articles on recent advances, technologies, solutions, applications, and new challenges in crop growth monitoring.

Potential topics include, but are not limited to, the following:

  • Crop modelling;
  • Crop growth modelling;
  • Processing of remote sensing data for agronomic information;
  • Artificial intelligence-based platforms;
  • Statistical methods to identify and evaluate the different factors affecting crop growth based on remote sensing data;
  • Use of remote sensing data to identify crop growth and the factors affecting it;
  • Machine learning applications in agriculture based on remote sensing.

Prof. Dr. Antonio Uris Martínez
Dr. Alberto Bautista
Guest Editors

Manuscript Submission Information

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Keywords

  • remote sensing
  • crop modelling
  • vegetation indices
  • machine learning
  • data processing
  • productivity
  • crop management

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Published Papers (1 paper)

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Research

19 pages, 5440 KiB  
Article
Evaluating UAV-Based Remote Sensing for Hay Yield Estimation
by Kyuho Lee, Kenneth A. Sudduth and Jianfeng Zhou
Sensors 2024, 24(16), 5326; https://doi.org/10.3390/s24165326 - 17 Aug 2024
Viewed by 450
Abstract
(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial [...] Read more.
(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel−1) and 50 m (35 mm pixel−1) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R2 values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)
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