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Advances in UAV Remote Sensing for Crop Monitoring and Yield Prediction

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Editors

Dr. Pedro Marques

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Guest Editor

Agronomy Department, School of Agrarian and Veterinary Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: remote sensing; irrigation and water management; computer engineering; machine learning; precision agriculture; olive tree; vineyard

Dr. Leilson Ferreira Gomes

E-Mail Website
Guest Editor

Agronomy Department, School of Agrarian and Veterinary Sciences, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: LiDAR; remote sensing; forest management; geoprocessing; precision agriculture

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have become an important platform for acquiring very high-resolution remote sensing data for agricultural monitoring. Recent advances in lightweight sensors, including RGB, multispectral, hyperspectral, thermal, and LiDAR systems, together with improvements in data processing and analysis techniques have expanded the capabilities of UAV remote sensing for characterizing crop conditions at fine spatial and temporal scales. These developments provide new opportunities to monitor crop growth, assess plant health, retrieve crop biophysical parameters, and support accurate yield prediction. Within the context of precision agriculture, UAV-based observations enable timely and flexible data acquisition, supporting improved crop management and more efficient use of agricultural resources.

This Special Issue aims to highlight recent advances in UAV remote sensing for crop monitoring and yield prediction. We welcome contributions presenting methodological developments, innovative data analysis approaches, and practical applications that improve the monitoring and understanding of crop systems using UAV observations. The topic of this Special Issue is aligned with the scope of Remote Sensing, particularly regarding the development and application of remote sensing technologies for environmental and agricultural monitoring.

Original research articles and review papers are invited on a wide range of topics, including but not limited to:

UAV remote sensing for crop monitoring and agricultural applications;
Retrieval of crop biophysical parameters and vegetation indices;
UAV-based crop phenotyping and plant trait analysis;
Detection of crop stress, diseases, and nutrient deficiencies;
Yield prediction and modelling using UAV data;
Machine learning and artificial intelligence applied to UAV imagery;
Multi-sensor data integration (e.g., RGB, multispectral, hyperspectral, thermal, LiDAR);
Multi-platform data fusion (e.g., UAV and satellite observations);
Time-series analysis of UAV imagery for crop growth monitoring;
High-resolution mapping and decision support for precision agriculture.

Researchers are encouraged to submit original research articles and review papers addressing recent advances in UAV remote sensing and its applications in agricultural monitoring and crop yield prediction.

Dr. Pedro Marques
Dr. Leilson Ferreira Gomes
Guest Editors

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22 pages, 10513 KB

Open AccessArticle

Maize Yield Prediction via Data Fusion of UAV Multi/Hyperspectral Imagery and In-Field Measurements

by Claudia Savarese, Marco De Mizio, Francesco Tufano, Davide Savy, Vincenzo Di Meo, Massimiliano Gargiulo, Sara Parrilli and Vincenza Cozzolino

Remote Sens. 2026, 18(15), 2460; https://doi.org/10.3390/rs18152460 - 27 Jul 2026

Viewed by 426

Abstract

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (

R^{2}

= 0.82; MAE = 6.36 q

{ha}^{- 1}

;

M A P E \approx

7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (

R^{2}

= 0.86; MAE = 6.56 q

{ha}^{- 1}

;

M A P E \approx

7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons. Full article

(This article belongs to the Special Issue Advances in UAV Remote Sensing for Crop Monitoring and Yield Prediction)

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