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Remote Sensing
Special Issues
Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods
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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 1291

Special Issue Editors

Prof. Dr. Renan Falcioni

E-Mail Website
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
Dr. Renato Herrig Furlanetto

E-Mail Website
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
Dr. Luis Crusiol

E-Mail Website
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 (3 papers)

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Research

25 pages, 5871 KiB  
Article
Estimating Wheat Traits Using Artificial Neural Network-Based Radiative Transfer Model Inversion
by Lukas J. Koppensteiner, Hans-Peter Kaul, Sebastian Raubitzek, Philipp Weihs, Pia Euteneuer, Jaroslav Bernas, Gerhard Moitzi, Thomas Neubauer, Agnieszka Klimek-Kopyra, Norbert Barta and Reinhard W. Neugschwandtner
Remote Sens. 2025, 17(11), 1904; https://doi.org/10.3390/rs17111904 - 30 May 2025
Viewed by 215
Abstract
Estimating wheat traits based on spectral reflectance measurements and machine learning remains challenging due to the large datasets required for model training and testing. To overcome this limitation, a simulated dataset was generated using the radiative transfer model (RTM) PROSAIL and inverted based [...] Read more.
Estimating wheat traits based on spectral reflectance measurements and machine learning remains challenging due to the large datasets required for model training and testing. To overcome this limitation, a simulated dataset was generated using the radiative transfer model (RTM) PROSAIL and inverted based on an artificial neural network (ANN). Field experiments were conducted in Eastern Austria to measure spectral reflectance and destructively sample plants to measure the wheat traits plant area index (PAI), nitrogen yield (NY), canopy water content (CWC), and above-ground dry matter (AGDM). Four ANN-based RTM inversion models were setup, which varied in their spectral resolution, hyperspectral or multispectral, and the inclusion or exclusion of background soil spectra correction. The models were also compared to a simple vegetation index approach using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge (NDRE). The RTM inversion model with hyperspectral input data and background soil spectra correction was the best among all tested models for estimating wheat traits during the vegetative developmental stages (PAI: R2 = 0.930, RRMSE = 17.9%; NY: R2 = 0.908, RRMSE = 14.4%; CWC: R2 = 0.967, RRMSE = 17.0%) as well as throughout the whole growing season (PAI: R2 = 0.845, RRMSE = 27.7%; CWC: R2 = 0.884, RRMSE = 20.0%; AGDM: R2 = 0.960, RRMSE = 13.7%). Many models presented in this study provided suitable estimations of the relevant wheat traits PAI, NY, CWC, and AGDM for application in agronomy, breeding, and crop sciences in general. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods)
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26 pages, 18550 KiB  
Article
Imaging of Leaf Water Patterns of Vitis vinifera Genotypes Infected by Plasmopara viticola
by Erich-Christian Oerke and Ulrike Steiner
Remote Sens. 2025, 17(10), 1788; https://doi.org/10.3390/rs17101788 - 20 May 2025
Viewed by 204
Abstract
The water status of plants is affected by abiotic and biotic environmental factors and influences the growth and yield formation of crops. Assessment of the leaf water content (LWC) of grapevine using hyperspectral imaging (1000–2500 nm) was investigated under controlled conditions for its [...] Read more.
The water status of plants is affected by abiotic and biotic environmental factors and influences the growth and yield formation of crops. Assessment of the leaf water content (LWC) of grapevine using hyperspectral imaging (1000–2500 nm) was investigated under controlled conditions for its potential to study the effects of the downy mildew pathogen Plasmopara viticola on LWC of host tissue in compatible and incompatible interactions. A calibration curve was established for the relationship between LWC and the Normalized Difference Leaf Water Index (NDLWI1937) that uses spectral information from the water absorption band and NIR for normalization. LWC was significantly lower for abaxial than for adaxial leaf sides, irrespective of grapevine genotype and health status. Reflecting details of leaf anatomy, vascular tissue exhibited effects reverse to intercostal areas. Effects of P. viticola on LWC coincided with the appearance of first sporangia on the abaxial side and increased during further pathogenesis. Continuous water loss ultimately resulted in tissue death, which progressed from the margins into central leaf areas. Tiny spots of brown leaf tissue related to the reaction of partial resistant cultivars could be monitored only at the sensor’s highest spatial resolution. Proximal sensing enabled an unprecedented spatial resolution of leaf water content in host–pathogen interactions and confirmed that resistance reactions may produce a combination of dead and still-living cells that enable the development of biotrophic P. viticola. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods)
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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 505
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
(This article belongs to the Special Issue Precision Agriculture and Crop Monitoring Based on Remote Sensing Methods)
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