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

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

Deadline for manuscript submissions: closed (19 July 2026) | Viewed by 43948

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Editors

Dr. Alexandre Maniçoba da Rosa Ferraz Jardim

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Department of Biodiversity, Institute of Biosciences, São Paulo State University—UNESP, Rio Claro 13506-900, SP, 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
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Dr. Luciana Sandra Bastos De Souza

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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.

Keywords

precision agriculture
forage crops
agricultural monitoring
image analysis
yield estimation
thermal imaging
machine learning
3D modeling
data modeling

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Published Papers (8 papers)

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Research

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21 pages, 9092 KB

Open AccessArticle

Prediction of Rice Brown Spot Disease Using Spectral Indices Derived from UAVs and Machine Learning Models in Lambayeque and Cajamarca, Peru

by Juan Valdiviezo, Jaime Aguilar-Lome, María Jaramillo-Carrión, Luis Ángel Ruiz and Lia Ramos-Fernández

Drones 2026, 10(7), 495; https://doi.org/10.3390/drones10070495 - 29 Jun 2026

Viewed by 507

Abstract

Rice brown spot, caused by Bipolaris oryzae, is an important constraint for rice production and requires timely field-scale monitoring. This study evaluated the use of multispectral bands acquired with a UAV-mounted sensor, together with vegetation indices, combined with machine-learning models to estimate rice brown spot severity under field conditions in Lambayeque and Cajamarca, Peru. A total of 37 sampling observations were collected across the vegetative, flowering, and milk-ripening stages. Spectral variables were extracted from UAV orthomosaics and related to field-based disease severity assessments. The strongest correlations with severity were observed for NDRE (r = −0.83) and NPCI (r = 0.77). Three regression models were evaluated using leave-one-out cross-validation (LOOCV): support vector regression with radial basis function kernel (SVR-rbf), support vector regression with linear kernel (SVR-linear), and Random Forest (RF). The SVR-linear model showed the lowest prediction error using NDRE, GREEN, and BLUE as predictors (R²_CV = 0.76; RMSE_CV = 1.31), although its performance was very similar to that of SVR-rbf and RF. These results indicate that UAV-derived multispectral information can support plot-level estimation of rice brown spot severity. However, model performance should be interpreted cautiously because of the small dataset, heterogeneous disease conditions, and moderate prediction accuracy. Further studies with larger and independent datasets are needed to improve robustness and transferability. 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)

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28 pages, 5747 KB

Open AccessArticle

Neural-Network Surrogate Framework for Rapid LCA Impact Screening of Potato Production: Manual Management vs. Drone-Assisted Technification

by Juan Carlos Almachi, Jessica Montenegro, Edwin Amaguaña, Danilo Arcentales and Esteban Valencia

Drones 2026, 10(5), 382; https://doi.org/10.3390/drones10050382 - 17 May 2026

Viewed by 787

Abstract

Potato cultivation in the Ecuadorian Andes is largely manual and relies on intensive agrochemical inputs. We introduce a reproducible workflow that couples life cycle assessment (LCA) with a neural-network surrogate to enable rapid multi-impact screening of two potato management scenarios in Ecuador: (i) conventional manual management and (ii) Unmanned aerial vehicle (UAV)-based field monitoring to identify hotspots for targeted ground-based input application. Multi-category impacts are computed in OpenLCA using the environmental footprint method (EF 3.0) per kilogram of potatoes and scaled to annual national totals using reported national production data. UAV operation is parameterized as 0.51 kg CO₂ eq·h⁻¹, equivalent to 0.225 kg CO₂ eq·ha⁻¹ at a coverage rate of 2.27 ha·h⁻¹. For 2024, the UAV-informed scenario reduces climate change from 4.29 × 10⁷ to 3.75 × 10⁷ kg CO₂ eq (−12.7%), resource use, fossils from 5.09 × 10⁸ to 4.54 × 10⁸ MJ (−10.7%), and freshwater eutrophication from 3.33 × 10⁴ to 2.83 × 10⁴ kg P eq (−15.0%), while land use remains nearly unchanged at ~4.73 × 10⁹ Pt (−0.1%). To avoid repeated LCA recalculations, a multi-output artificial neural network (ANN) surrogate (29 outputs) was trained in Python (TensorFlow/Keras) and evaluated using leave-one-year-out (LOYO) cross-validation (2015–2024), showing strong agreement with the LCA results. This framework enables scalable what-if analysis and efficient evaluation of UAV-enabled precision monitoring strategies in resource-constrained settings. 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)

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26 pages, 9482 KB

Open AccessArticle

Can Environmental Analysis Algorithms Be Improved by Data Fusion and Soil Removal for UAV-Based Buffel Grass Biomass Prediction?

by Wagner Martins dos Santos, Alexandre Maniçoba da Rosa Ferraz Jardim, Lady Daiane Costa de Sousa Martins, Márcia Bruna Marim de Moura, Elania Freire da Silva, Luciana Sandra Bastos de Souza, Alan Cezar Bezerra, José Raliuson Inácio Silva, Ênio Farias de França e Silva, João L. M. P. de Lima, Leonor Patricia Cerdeira Morellato and Thieres George Freire da Silva

Drones 2026, 10(1), 61; https://doi.org/10.3390/drones10010061 - 15 Jan 2026

Viewed by 702

Abstract

The growing demand for sustainable livestock systems requires efficient methods for monitoring forage biomass. This study evaluated spectral (RGB and multispectral), textural (GLCM), and area attributes derived from unmanned aerial vehicle (UAV) imagery to predict buffelgrass (Cenchrus ciliaris L.) biomass, also testing the effect of soil pixel removal. A comprehensive machine learning pipeline (12 algorithms and 6 feature selection methods) was applied to 14 data combinations. Our results demonstrated that soil removal consistently improved the performance of the applied models. Multispectral (MSI) sensors were the most robust individually, whereas textural (GLCM) attributes did not contribute significantly. Although the MSI and RGB data combination proved complementary, the model with the highest accuracy was obtained with CatBoost using only RGB information after Boruta feature selection, achieving a CCC of 0.83, RMSE of 0.214 kg, and R² of 0.81 in the test set. The most important variable was vegetation cover area (19.94%), surpassing spectral indices. We conclude that integrating RGB UAVs with robust processing can generate accessible and effective tools for forage monitoring. This approach can support pasture management by optimizing stocking rates, enhancing natural resource efficiency, and supporting data-driven decisions in precision silvopastoral systems. 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)

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21 pages, 6044 KB

Open AccessArticle

Estimation of Cotton LAI and Yield Through Assimilation of the DSSAT Model and Unmanned Aerial System Images

by Hui Peng, Esirige, Haibin Gu, Ruhan Gao, Yueyang Zhou, Xinna Men and Ze Wang

Drones 2026, 10(1), 27; https://doi.org/10.3390/drones10010027 - 3 Jan 2026

Cited by 1 | Viewed by 1164

Abstract

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management. 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)

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14 pages, 7214 KB

Open AccessArticle

Agroecological Alternatives for Substitution of Glyphosate in Orange Plantations (Citrus sinensis) Using GIS and UAVs

by María Guadalupe Galindo Mendoza, Abraham Cárdenas Tristán, Pedro Pérez Medina, Rita Schwentesius Rindermann, Tomás Rivas García, Carlos Contreras Servín and Oscar Reyes Cárdenas

Drones 2025, 9(6), 398; https://doi.org/10.3390/drones9060398 - 28 May 2025

Cited by 1 | Viewed by 2623

Abstract

Field mapping is one of the most important aspects of precision agriculture, and community drones will be able to empower young rural entrepreneurs who will be the generational replacement of a new agrosocial paradigm. This research presents an agroecological participatory innovation methodology that utilizes precision technology through geographic information systems and unmanned aerial vehicles to evaluate the integrated ecological management of weeds for glyphosate substitution in a transitional area of Citrus sinensis in San Luis Potosí, Mexico. Modeling methods and spatial analyses supported by intelligent georeference protocols were used to determine the number of weeds with tolerance and glyphosate resistance. Four control flights were conducted to monitor seven treatments. Glyphosate-resistant weeds were represented with the highest number of individuals and frequency in all experimental treatments. Although the treatment with maize stubble showed a slightly better result than the use of Mucuna pruriens mulch, which prevents the emergence of glyphosate resistant weeds before emergence, the second treatment is considered better in terms of the cost–benefit ratio, not only because of significantly lower cost but also because of the additional benefits it offers. Geospatial technologies will determine the nature of citrus and fruit tree agroecological treatments and highlight areas of the plot with binomial soil and plant nutrient deficiencies and pest and disease infestations, which will improve the timely application of bio-inputs through the development of accurate maps of agroecological transitions. 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)

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25 pages, 8232 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1345

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 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)

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21 pages, 13154 KB

Open AccessFeature PaperArticle

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

Cited by 6 | Viewed by 4203

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 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)

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Review

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25 pages, 4811 KB

Open AccessReview

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 122 | Viewed by 30853

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 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)

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