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Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods

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: 14 November 2025 | Viewed by 263

Special Issue Editors


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Guest Editor
Department of Agronomy, State University of Maringa, Maringa 87020-900, Brazil
Interests: plant physiology; remote sensing; hyperspectral data; multivariate statistical analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Weed Science Laboratory, Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
Interests: precision agriculture; crop monitoring; remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Embrapa Soja, National Soybean Research Center, Brazilian Agricultural Research Corporation, Londrina 86085-981, Brazil
Interests: multispectral;, hyperspectral; thermal imaging system; drought monitoring; spectral data processing; UAV systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture is pivotal in optimizing crop yields, managing resources more efficiently, minimizing environmental impacts, and diagnosing crop health issues using remote sensing techniques. Recent advancements in sensor technologies and novel data processing methods are unlocking new possibilities for modern agricultural practices. For the upcoming Special Issue “Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods”, we invite researchers to submit original research articles, comprehensive reviews, and insightful case studies focusing on cutting-edge applications of remote sensing technologies in agriculture. We seek contributions that showcase innovative uses of satellite imagery, UAVs (drones), multispectral and hyperspectral sensors, LiDAR, and radar for various agricultural tasks, including crop monitoring, soil analysis, pest and disease detection, water resource management, and yield forecasting. We particularly encourage submissions that explore, but are not limited to, the integration of artificial intelligence (AI) and machine learning (ML) in data analysis or those that discuss the challenges, limitations, and future potential of remote sensing in transforming agriculture. Contributions are encouraged in, but not limited to, the following areas:

Crop Monitoring and Management: Studies demonstrating advanced techniques for assessing crop growth, health, and development in real-time, using remote sensing data to optimize input management and improve yield outcomes.

Soil Analysis: Research on the integration of remote sensing technologies to assess soil properties, moisture levels, nutrient availability, and other parameters critical for soil health and crop productivity.

Pest and Disease Detection: Innovative approaches that utilize remote sensing methods for early detection and monitoring of pests, diseases, and other biotic stresses that affect crop health and yield, with a focus on precision intervention.

Water Resource Management: Applications of remote sensing in monitoring and optimizing irrigation practices, improving water use efficiency, and managing water resources for sustainable agriculture in both rainfed and irrigated systems.

Yield Prediction and Forecasting: Case studies and research focused on the application of remote sensing to model and predict crop yields, helping farmers and policymakers make informed decisions about resource allocation and market trends.

Prof. Dr. Renan Falcioni
Guest Editor

Dr. Renato Herrig Furlanetto
Dr. Luis Crusiol
Guest Editor Assistants

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

  • precision agriculture
  • remote sensing
  • crop monitoring
  • satellite imagery
  • UAVs (drones)
  • multispectral sensing
  • hyperspectral imaging
  • LiDAR in agriculture
  • AI in agriculture
  • sustainable farming.

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Published Papers (1 paper)

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Research

17 pages, 4303 KiB  
Article
Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing
by Anish Bhattarai, Gonzalo J. Scarpin, Amrinder Jakhar, Wesley Porter, Lavesta C. Hand, John L. Snider and Leonardo M. Bastos
Remote Sens. 2025, 17(9), 1504; https://doi.org/10.3390/rs17091504 - 24 Apr 2025
Viewed by 193
Abstract
Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (Gossypium hirsutum L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing [...] Read more.
Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (Gossypium hirsutum L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing yield. The main goal of this study was to determine the optimal unmanned aerial vehicle flight settings—altitude and speed—and assess specific processing parameters’ impact on data accuracy, processing time, and file size. Nine flight settings comprising three altitudes (12.2 m, 24.4 m, and 48.8 m) and three speeds (4.8 km/h, 9.6 km/h, and 14.4 km/h) were tested. LiDAR data were processed using DJI Terra software (v. 4.1.0), where two user-defined processing steps were examined: point-cloud thinning via grid size sub-sampling (0, 10, 20, 30, 40, and 50 cm) and slope classification (flat, gentle, and steep). The optimal flight altitude was 24.4 m, with no effect of flight speed. Grid sub-sampling up to 20 cm produced balanced accuracy, processing time, and file size. The choice of slope category had no significant effect on LiDAR-derived canopy height. These findings contribute to the development of standardized LiDAR data acquisition and processing guidelines for cotton to support crop management decision. Full article
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