Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
4.4
Journals
Drones
Special Issues
Advances in the Use of UAVs for Monitoring and Analysis...
share announcement

Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".

Deadline for manuscript submissions: 21 November 2025 | Viewed by 9220

Special Issue Editors

Dr. Alexandre Maniçoba da Rosa Ferraz Jardim

E-Mail Website
Guest Editor
Department of Biodiversity, Institute of Biosciences, São Paulo State University-UNESP, Av. 24A, 1515, Rio Claro 13506-900, São Paulo, Brazil
Interests: abiotic stresses; salinity; physiological and biochemical changes in plants; salt stress tolerance; reactive oxygen species (ROS); ion homeostasis; ion toxicity; agricultural challenges in arid and semi-arid regions
Special Issues, Collections and Topics in MDPI journals
Dr. Luciana Sandra Bastos De Souza

E-Mail Website
Guest Editor
Academic Unit of Serra Talhada, Rural Federal University of Pernambuco, Serra Talhada 56909-535, Pernambuco, Brazil
Interests: semi-arid region; intercropping; irrigation; evapotranspiration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the growing global demand for sustainable and efficient agricultural solutions has positioned unmanned aerial vehicles (UAVs) as essential for monitoring agricultural parameters, particularly forage and crop cultivation. This Special Issue invites submissions on the application of UAVs equipped with various payloads for precisely capturing imaging data and detailed analysis of forage and crop cultivation. The aim is to explore how UAV technology can identify growth patterns, productivity, and crop interactions, thereby contributing to developing more sustainable agricultural strategies.

In this Special Issue, we seek to advance the state of the art in UAV applications in precision agriculture, particularly for forage and crop cultivation, by enhancing data acquisition and crop analysis. We encourage original research articles that present new methods or applications of UAVs in precision agriculture and systematic reviews that consolidate current knowledge on the use of UAVs in forage and crop cultivation.

We look forward to receiving your original research articles and reviews.

Suggested topics:

  • Extraction of information from images captured by UAVs, including vegetation indices, texture analysis, and 3D modeling.
  • Machine learning applications for predicting, classifying, and identifying plant species, pests, and diseases in forage or agricultural crops.
  • Estimating productivity, biophysical, biochemical, and physiological parameters, comparing UAV-derived data with traditional field measurements.
  • Development of methodologies for data fusion between UAV images and satellite or ground-based products.
  • Other applications involving UAVs in forage and/or crops.

Dr. Alexandre Maniçoba da Rosa Ferraz Jardim
Dr. Luciana Sandra Bastos De Souza
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. Drones is an international peer-reviewed open access monthly 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 2600 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
  • forage crops
  • agricultural monitoring
  • image analysis
  • yield estimation
  • thermal imaging
  • machine learning
  • 3D modeling
  • data modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

25 pages, 8232 KiB  
Article
Estimation of Amorphophallus Konjac Above-Ground Biomass by Integrating Spectral and Texture Information from Unmanned Aerial Vehicle-Based RGB Images
by Ziyi Yang, Hongjuan Qi, Kunrong Hu, Weili Kou, Weiheng Xu, Huan Wang and Ning Lu
Drones 2025, 9(3), 220; https://doi.org/10.3390/drones9030220 - 19 Mar 2025
Viewed by 224
Abstract
The estimation of Above-Ground Biomass (AGB) in Amorphophallus konjac (Konjac) is essential for field management and yield prediction. While previous research has demonstrated the efficacy of Unmanned Aerial Vehicle (UAV) RGB imagery in estimating AGB for monoculture crops, the applicability of these methods [...] Read more.
The estimation of Above-Ground Biomass (AGB) in Amorphophallus konjac (Konjac) is essential for field management and yield prediction. While previous research has demonstrated the efficacy of Unmanned Aerial Vehicle (UAV) RGB imagery in estimating AGB for monoculture crops, the applicability of these methods to AGB estimation in Konjac remains uncertain due to its distinct morphological traits and prevalent intercropping practices with maize. Additionally, the Vegetation Indices (VIs) and Texture Features (TFs) obtained from UAV-based RGB imagery exhibit significant redundancy, raising concerns about whether the selected optimal variables can maintain estimation accuracy. Therefore, this study assessed the effectiveness of Variable Selection Using Random Forests (VSURF) and Principal Component Analysis (PCA) in variable selection and compared the performance of Stepwise Multiple Linear Regression (SMLR) with four Machine Learning (ML) regression techniques: Random Forest Regression (RFR), Extreme Gradient Boosting Regression (XGBR), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), as well as Deep Learning (DL), in estimating the AGB of Konjac based on the selected features. The results indicate that the integration (PCA_(PCA_VIs+PCA_TFs)) of PCA-based VIs and PCA-based TFs using PCA achieved the best prediction accuracy (R2 = 0.96, RMSE = 0.08 t/hm2, MAE = 0.06 t/hm2) with SVR. In contrast, the DL model derived from AlexNet, combined with RGB imagery, yielded moderate predictive accuracy (R2 = 0.72, RMSE = 0.21 t/hm2, MAE = 0.17 t/hm2) compared with the optimal ML model. Our findings suggest that ML regression techniques, combined with appropriate variable-selected approaches, outperformed DL techniques in estimating the AGB of Konjac. This study not only provides new insights into AGB estimation in Konjac but also offers valuable guidance for estimating AGB in other crops, thereby advancing the application of UAV technology in crop biomass estimation. Full article
(This article belongs to the Special Issue Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications)
►▼ Show Figures

Figure 1

21 pages, 13154 KiB  
Article
Cover Crop Biomass Predictions with Unmanned Aerial Vehicle Remote Sensing and TensorFlow Machine Learning
by Aakriti Poudel, Dennis Burns, Rejina Adhikari, Dulis Duron, James Hendrix, Thanos Gentimis, Brenda Tubana and Tri Setiyono
Drones 2025, 9(2), 131; https://doi.org/10.3390/drones9020131 - 11 Feb 2025
Viewed by 834
Abstract
The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural [...] Read more.
The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural data, which allows for effective evaluation of cover crop biomass. Vegetation indices are widely used to quantify crop growth and biomass metrics. The objective of this study was to evaluate the accuracy of biomass estimation using a machine learning approach leveraging spectral and canopy height data acquired from unmanned aerial vehicles (UAVs), comparing different neural network architectures, optimizers, and activation functions. Field trials were carried out at two sites in Louisiana involving winter cover crops. The canopy height was estimated by subtracting the digital surface model taken at the time of peak growth of the cover crop from the data captured during a bare ground condition. When evaluated against the validation dataset, the neural network model facilitated with a Keras TensorFlow library with Adam optimizers and a sigmoid activation function performed the best, predicting cover crop biomass with an average of 96 g m−2 root mean squared error (RMSE). Other statistical metrics including the Pearson correlation and R2 also showed satisfactory conditions with this combination of hyperparameters. The observed cover crop biomass ranged from 290 to 1217 g m−2. The present study findings highlight the merit of comprehensive analysis of cover crop traits using UAV remote sensing and machine learning involving realistic underpinning biophysical mechanisms, as our approach captured both horizontal (vegetation indices) and vertical (canopy height) aspects of plant growth. Full article
(This article belongs to the Special Issue Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications)
►▼ Show Figures

Figure 1

Review

Jump to: Research

25 pages, 4811 KiB  
Review
Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture
by Juhi Agrawal and Muhammad Yeasir Arafat
Drones 2024, 8(11), 664; https://doi.org/10.3390/drones8110664 - 10 Nov 2024
Cited by 9 | Viewed by 7559
Abstract
The integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) and machine learning (ML) has fundamentally transformed precision agriculture by enhancing efficiency, sustainability, and data-driven decision making. In this paper, we present a comprehensive overview of the integration of multispectral, hyperspectral, and [...] Read more.
The integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) and machine learning (ML) has fundamentally transformed precision agriculture by enhancing efficiency, sustainability, and data-driven decision making. In this paper, we present a comprehensive overview of the integration of multispectral, hyperspectral, and thermal sensors mounted on drones with AI-driven algorithms to transform modern farms. Such technologies support crop health monitoring in real time, resource management, and automated decision making, thus improving productivity with considerably reduced resource consumption. However, limitations include high costs of operation, limited UAV battery life, and the need for highly trained operators. The novelty of this study lies in the thorough analysis and comparison of all UAV-AI integration research, along with an overview of existing related works and an analysis of the gaps. Furthermore, practical solutions to technological challenges are summarized to provide insights into precision agriculture. This paper also discusses the barriers to UAV adoption and suggests practical solutions to overcome existing limitations. Finally, this paper outlines future research directions, which will discuss advances in sensor technology, energy-efficient AI models, and how these aspects influence ethical considerations regarding the use of UAVs in agricultural research. Full article
(This article belongs to the Special Issue Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop