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AgriEngineering
Special Issues
Application of Remote Sensing and Machine Learning in Precision Agriculture
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Application of Remote Sensing and Machine Learning in Precision Agriculture

A Special Issue of AgriEngineering (ISSN 2624-7402) belonging to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: 15 April 2027 | Viewed by 3468

Editors

Dr. Maílson Freire de Oliveira

E-Mail Website
Guest Editor
Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
Interests: precision agriculture; machine learning; remote sensing
Prof. Dr. Rouverson Pereira da Silva

E-Mail Website1 Website2
Guest Editor
Department of Engineering, São Paulo State University (UNESP), Jaboticabal 14884-900, SP, Brazil
Interests: digital mechanization; precision agriculture; smart harvesting systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jarlyson Brunno Costa Souza

E-Mail Website
Guest Editor
Department of Agricultural Engineering, Federal University of Maranhão, Chapadinha 65500-000, MA, Brazil
Interests: precision agriculture; artificial intelligence; spray drones

Special Issue Information

Dear Colleagues,

The increasing demand for efficient and environmentally sustainable agricultural systems has accelerated the adoption of remote sensing and machine learning technologies in precision agriculture. Advances in satellite, UAV, and proximal sensing now enable detailed monitoring of crops and soils across multiple spatial and temporal scales. When combined with machine learning and deep learning approaches, these data provide powerful tools to support site-specific management and data-driven decision-making.

This Special Issue, “Application of Remote Sensing and Machine Learning in Precision Agriculture,” aims to present recent methodological developments and practical applications that exploit remote sensing data and machine learning techniques to enhance agricultural productivity and sustainability. Original research articles are welcome.

Topics of interest include, but are not limited to:

  • Machine learning and deep learning methods for agricultural remote sensing;
  • Crop growth monitoring, phenology analysis, and yield prediction using satellite and UAV data;
  • Detection of crop stress, diseases, pests, and water or nutrient limitations;
  • Soil property mapping and soil–crop interactions using remote sensing and ML;
  • Precision irrigation and nutrient management supported by remote sensing analytics;
  • Multisensor and multiscale data fusion (optical, hyperspectral, thermal, SAR, LiDAR);
  • Time-series analysis of cropland dynamics and management practices;
  • Model validation, benchmarking, uncertainty analysis, and operational case studies.

We look forward to receiving your valuable contributions.

Dr. Maílson Freire de Oliveira
Prof. Dr. Rouverson Pereira da Silva
Dr. Jarlyson Brunno Costa 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering 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 1800 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

  • machine learning
  • remote sensing
  • drones
  • satellite
  • digital agriculture
  • precision agriculture
  • neural networks
  • crop yield prediction

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

Published Papers (4 papers)

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Research

23 pages, 2812 KB  
Article
Assessing UAV-Acquired RGB, Multispectral, and Hyperspectral Imagery for Crop Residue Cover Mapping Using a Fully Connected Neural Network
by Lilian Yang, Bing Lu, Margaret Schmidt, Shujian Jin, Ali Jamali and David McCaffrey
AgriEngineering 2026, 8(8), 333; https://doi.org/10.3390/agriengineering8080333 - 11 Aug 2026
Viewed by 313
Abstract
Accurate mapping of crop residue cover (CRC) is important for sustainable agricultural management because residue supports soil health, erosion control, and carbon sequestration. Traditional ground-based CRC methods are labor-intensive and spatially limited, increasing interest in UAV-based remote sensing. UAVs can carry RGB, multispectral, [...] Read more.
Accurate mapping of crop residue cover (CRC) is important for sustainable agricultural management because residue supports soil health, erosion control, and carbon sequestration. Traditional ground-based CRC methods are labor-intensive and spatially limited, increasing interest in UAV-based remote sensing. UAVs can carry RGB, multispectral, and hyperspectral sensors, which capture different spectral information for distinguishing crop residue from soil. This study compared four high-spatial-resolution (2.5 cm) UAV imagery types—RGB, multispectral, visible–near-infrared (VNIR) hyperspectral, and shortwave infrared (SWIR) hyperspectral—for fine-scale CRC classification. A fully connected neural network (FCNN) was developed to classify residue and soil pixels. Performance was evaluated using two complementary approaches: pixel-level accuracy assessment based on manually delineated image samples and plot-level validation against residue percentages derived from ground photos. Results showed that high pixel-level classification accuracy values were achieved across all imagery types, with overall accuracies above 94%. However, plot-level validation revealed that sensor performance depended on the evaluation metric considered. Multispectral imagery produced the highest R2 with ground photo-derived reference CRC values (R2 = 0.672). These results indicate that greater spectral dimensionality did not necessarily improve plot-level CRC estimation under the tested field conditions. More importantly, the findings show that high pixel-level classification accuracy does not necessarily translate into stronger plot-level CRC estimation, highlighting the importance of using complementary validation approaches when evaluating UAV-based CRC estimation. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Machine Learning in Precision Agriculture)
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24 pages, 20818 KB  
Article
Enhancing Rice Yield Prediction Through Cross-Sensor Time-Series Integration
by Javier Quille-Mamani, José Huanuqueño-Murillo, Lia Ramos-Fernández and Luis Ángel Ruiz
AgriEngineering 2026, 8(8), 316; https://doi.org/10.3390/agriengineering8080316 - 29 Jul 2026
Viewed by 933
Abstract
Field-scale rice yield prediction in irrigated coastal systems is constrained by small plot size, persistent cloud cover, and strong interannual variability, which limit the effectiveness of single-sensor remote sensing approaches. This study proposes an integrated framework that combines multispectral time series from Sentinel-2, [...] Read more.
Field-scale rice yield prediction in irrigated coastal systems is constrained by small plot size, persistent cloud cover, and strong interannual variability, which limit the effectiveness of single-sensor remote sensing approaches. This study proposes an integrated framework that combines multispectral time series from Sentinel-2, PlanetScope, and unmanned aerial vehicle (UAV) platforms with phenological metrics derived from the Normalized Difference Vegetation Index (NDVI), climate variables aggregated within phenology-defined windows, and machine learning algorithms to predict rice grain yield at the plot level. The framework was structured in five phases: (i) cross-sensor consistency assessment of red, near-infrared, and NDVI values across platform pairs; (ii) linear harmonization and multisource temporal fusion of NDVI time series at 5-day intervals; (iii) extraction of phenological metrics from smoothed NDVI trajectories using a relative-threshold approach; (iv) aggregation of meteorological variables within crop-stage-specific windows; and (v) yield prediction using partial least squares regression (PLSR), Random Forest, and XGBoost under nested leave-one-out cross-validation. The framework was evaluated on 72 irrigated rice plots (37 in 2022, 35 in 2023) in Lambayeque, northern Peru. Cross-sensor analysis revealed that the PlanetScope–UAV pair achieved the strongest NDVI agreement (R2=0.87, RMSE =0.07), while Sentinel-2–PlanetScope showed higher correlation (R2=0.91) but with systematic bias requiring calibration. Multi-source fusion raised temporal coverage from 53–62% (individual sensors) to 82% in 2022 and 66% in 2023. The best single-season prediction was obtained in 2022 with PlanetScope-derived phenological metrics and XGBoost (Rcv2=0.72, RMSEcv=1.23 t ha−1), while the best cross-season performance was achieved with combined phenological and climate features using the PlanetScope+UAV configuration and XGBoost (Rcv2=0.64, RMSEcv=1.35 t ha−1). SHAP-based interpretability analysis identified post-peak phenological descriptors and climatic conditions during the grain-filling window as the most informative predictors. These findings demonstrate that PlanetScope-centered multi-source fusion, combined with phenology-informed feature engineering, provides a robust basis for rice yield prediction in cloud-prone irrigated environments. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Machine Learning in Precision Agriculture)
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Graphical abstract

20 pages, 9790 KB  
Article
Evaluation of the Relationship Between the Level of UVB Irradiation and the Reflectance Spectrum of Leaves and the Content of Steviol Glycosides in Stevia rebaudiana Bertoni
by Alexey P. Dolgalev, Alexander A. Smirnov, Yuri A. Proshkin, Pavel V. Tikhonov, Dmitry A. Burynin, Inna V. Knyazeva, Alina S. Ivanitskikh and Alexander V. Sokolov
AgriEngineering 2026, 8(7), 258; https://doi.org/10.3390/agriengineering8070258 - 24 Jun 2026
Viewed by 360
Abstract
Stevia (Stevia rebaudiana Bertoni) is an important source of natural sweeteners. Since its commercial value depends on steviol glycosides, quality assessment primarily involves quantifying these compounds in leaves and shoots. While chromatography is the standard analytical method, it is labor-intensive and time-consuming; [...] Read more.
Stevia (Stevia rebaudiana Bertoni) is an important source of natural sweeteners. Since its commercial value depends on steviol glycosides, quality assessment primarily involves quantifying these compounds in leaves and shoots. While chromatography is the standard analytical method, it is labor-intensive and time-consuming; it involves multiple processing steps that may cumulatively introduce errors and remains relatively expensive. Although chromatography remains the most accurate method, this exploratory study evaluates the potential of using spectroscopy as an auxiliary method for the approximate assessment of steviol glycoside content. Leaf reflectance spectroscopy could be a simpler and more cost-effective approach. However, relationships between leaf reflectance and steviol glycoside content are indirect and mediated by physiological processes. To account for these indirect dependencies, cumulative UVB exposure was included as an additional feature because it influences both leaf optical properties and plant metabolic processes. A low-cost spectrometer was utilized as the measuring instrument. The study was conducted over a period of three months on 77 S. rebaudiana clones, divided into four groups based on their level of UVB irradiance (control without irradiation, 400, 600, and 800 μW m−2). Based on the collected data, linear and polynomial regression, Random Forest, XGBoost, PLSR, and ElasticNetCV models were trained. Cumulative UVB exposure was found to be the most important feature. Of the spectral features, the most informative for assessing the content of steviol glycosides were spectral indicators in the far-red and near-infrared (NIR) ranges. Our results indicate a detectable relationship, with Random Forest being the best-performing model and achieving a moderate predictive performance (R2 = 0.66). Despite their limited predictive performance, the models demonstrate that leaf reflectance spectra combined with cumulative UVB exposure contain information related to steviol glycoside content. These findings support further investigation of remote sensing approaches for crop quality assessment. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Machine Learning in Precision Agriculture)
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26 pages, 6352 KB  
Article
Deep Learning–Based Corn Yield Component Estimation Under Different Nitrogen and Irrigation Rates
by Binita Ghimire, Lorena N. Lacerda, Thirimachos Bourlai and Guoyu Lu
AgriEngineering 2026, 8(4), 146; https://doi.org/10.3390/agriengineering8040146 - 9 Apr 2026
Viewed by 1286
Abstract
The number of kernels per ear is a key yield parameter that reflects the effects of breeding and agronomic management practices on crop productivity. However, conventional manual counting is labor-intensive, time-consuming, and prone to human error. This study evaluated the performance of six [...] Read more.
The number of kernels per ear is a key yield parameter that reflects the effects of breeding and agronomic management practices on crop productivity. However, conventional manual counting is labor-intensive, time-consuming, and prone to human error. This study evaluated the performance of six YOLO models, trained from scratch and fine-tuned, alongside a Faster R-CNN model, for automated kernel detection and counting from manually harvested field corn ear images. Model performance was assessed for predicting the yield and harvest index (HI) of field corn under varying nitrogen and irrigation rates. Results show that models trained with fine-tuning consistently outperform those trained from scratch in both accuracy and computational speed. Among all tested YOLO models, YOLOv11x achieved the highest performance, with a precision of 0.978, a recall of 0.968, a latency of 4.8 ms, and a prediction coefficient of determination (R2pred) of 0.858 for the test set and 0.890 for cross-year datasets. The YOLOv8x model ranked second, whereas YOLOv10x was the worst-performing model. Compared to YOLO, Faster R-CNN performed poorly. Yield and HI predictions using YOLOv11x achieved R2 values of 0.881 and 0.758, respectively, and captured treatment effects. Overall, the findings demonstrate that YOLO-based architecture is highly effective for detecting kernels and predicting yield in precision agriculture applications. Full article
(This article belongs to the Special Issue Application of Remote Sensing and Machine Learning in Precision Agriculture)
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