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AgriEngineering
Special Issues
Remote Sensing for Enhanced Agricultural Crop Management
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Remote Sensing for Enhanced Agricultural Crop Management

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2102

Special Issue Editors

Dr. Pedro V. Mauri

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Guest Editor
Agro-Environmental Research Area, Madrid Institute for Rural, Agricultural, and Food Research and Development (IMIDRA), El Encín, A-2 Highway, Km. 38.200, Alcalá de Henares, 28805 Madrid, Spain
Interests: precision agriculture; crop management; green areas; PAM; bioenergy; crop propagation; agriculture; environment; sustainability; remote sensing methods; forest production systems; forest nursery
Special Issues, Collections and Topics in MDPI journals
Dr. Lorena Parra

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Guest Editor
Departamento de Producción Agraria, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Avenida Puerta de Hierro, 4, 28040 Madrid, Spain
Interests: environmental monitoring; precision agriculture; remote sensing; image processing; crop management; smart cities; green areas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Continuous advances in monitoring technologies, and particularly in remote sensing, have spurred a great revolution in agricultural activity. Remote sensing has become a key tool in modern agriculture, enabling the non-invasive, affordable, and high-frequency monitoring of crop conditions, soil properties, and environmental factors. Through the use of spectral data and vegetation indices, remote sensing facilitates the early detection of biotic and abiotic stress, the assessment of biomass, yield estimation, and the optimization of inputs such as water and fertilizers, among others. Of the available platforms, satellite-based remote sensing tools enable wide coverage and historical data series. In contrast, unmanned aerial vehicles (UAVs or drones) provide high-resolution, on-demand imagery, and great flexibility in terms of flight planning, making them especially suited for plot-level analysis and precision agriculture interventions. UAVs can be equipped with RGB, multispectral, thermal, or LiDAR sensors to support tasks ranging from disease detection to canopy structure modelling. The combination of both satellite and drone-based data within proximal sensing monitoring systems represents a promising path to robust, multi-scale agricultural decision support tools. The aim of this Special Issue is to showcase recent advances in the development and application of remote sensing technologies and tools, from satellite platforms to UAV-based systems, for improving crop monitoring, resource management, and decision-making in agricultural systems under current and future challenges.

The scope of this Special Issue covers a broad range of topics, including various remote sensing platforms and monitoring sensors, as well as their applications across multiple crop types. We encourage the submission of original research articles, reviews, and perspectives that present and analyze innovative approaches to improving agricultural management. Research areas may include, but are not limited to, the following:

  • Innovative applications of remote sensing for agricultural monitoring and management.
  • The application of remote sensing in precision farming.
  • Comparative assessments of satellite and UAV data for agricultural decision-making.
  • Multitemporal analysis for change detection in agricultural areas.
  • The evaluation of image processing techniques for the identification of biotic and abiotic stress.
  • The use of remote sensing data combined with artificial intelligence for decision-making or automatic classification in precision agriculture.
  • Point cloud-based solutions for fruit tree management.
  • The integration of remote sensing with proximal sensing tools for enhanced agronomic GIS.

Dr. Pedro V. Mauri
Dr. Lorena Parra
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-blind 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 1600 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 farming
  • agronomy
  • drone
  • satellite
  • unmanned aerial vehicle
  • stress
  • yield

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Published Papers (3 papers)

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Research

28 pages, 3973 KB  
Article
Economic Impact of Optical Sensors and Deep Learning in Smart Agriculture: A Scientometric Analysis
by Nini Johana Marín-Rodríguez, Juan David Gonzalez-Ruiz and Sergio Botero
AgriEngineering 2025, 7(12), 397; https://doi.org/10.3390/agriengineering7120397 - 28 Nov 2025
Viewed by 406
Abstract
The integration of optical sensors and deep learning technologies in smart agriculture represents a critical intersection between technological innovation and agricultural economic sustainability, yet comprehensive assessments of their economic impact remain limited. This study applies a scientometric approach to 135 documents indexed in [...] Read more.
The integration of optical sensors and deep learning technologies in smart agriculture represents a critical intersection between technological innovation and agricultural economic sustainability, yet comprehensive assessments of their economic impact remain limited. This study applies a scientometric approach to 135 documents indexed in Scopus and Web of Science between January 2017 and June 2025, using Bibliometrix Bibliometrix (R package version 4.5.2), VOSviewer version 1.6.20, and Voyant Tools to examine publication trends, leading contributors, collaboration patterns, thematic structures, and reported economic outcomes. The analysis shows a strong upward trajectory with an estimated 66.48% annual increase in publications, identifying Xiukang Wang and Shaowen Wang as leading contributors among 791 authors from diverse institutions. Thematic analysis reveals three interconnected clusters: (i) precision agriculture and remote sensing as the sensing backbone; (ii) prediction and soil analysis as data-driven decision-support mechanisms; and (iii) vegetation indexes and productivity as measurement tools linking spectral information to yield and input use. Economic evidence includes high disease-detection accuracy (up to 95%), notable pesticide-use reductions (around 40%), improved autonomous-navigation precision (<6 cm error), and crop-detection performance exceeding 99%. However, adoption challenges persist, including technological heterogeneity, high implementation costs, limited model transferability, and varying levels of digital readiness across regions. Overall, the findings indicate that optical sensors and deep learning are transitioning from experimental applications to technologies with measurable economic impact, offering guidance for researchers, policymakers, technology developers, and agricultural producers seeking economically viable precision-agriculture solutions. Full article
(This article belongs to the Special Issue Remote Sensing for Enhanced Agricultural Crop Management)
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18 pages, 5256 KB  
Article
Cotton Yield Map Prediction Using Sentinel-2 Satellite Imagery in the Brazilian Cerrado Production System
by Carlos Manoel Pedro Vaz, Ednaldo José Ferreira, Eduardo Antônio Speranza, Júlio César Franchini, João de Mendonça Naime, Ricardo Yassushi Inamasu, Ivani de Oliveira Negrão Lopes, Sérgio das Chagas, Mathias Xavier Schelp, Leonardo Vecchi and Rafael Galbieri
AgriEngineering 2025, 7(11), 390; https://doi.org/10.3390/agriengineering7110390 - 16 Nov 2025
Viewed by 743
Abstract
Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in large-scale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, [...] Read more.
Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in large-scale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, and complex post-processing. Orbital remote sensing offers an alternative by providing consistent vegetation index (VI) data for crop monitoring and yield estimation. This study developed regression models relating Sentinel-2 VIs (EVI, TVI, NDVI, and NDRE) to cotton yield data obtained from combine harvesters across 30 commercial plots in Mato Grosso, Brazil, over six cropping seasons (2019–2024), totaling 76 plot-season datasets. Vegetation indices were grouped into 15-day intervals based on days after sowing, and a logistic growth function was applied in the regression modeling. Model performance evaluated using 15 independent plot-seasons showed good pixel-level accuracy, with RMSE of 0.695 t ha−1 and R2 of 0.78, with EVI performing slightly better. At the plot scale, mean yield predictions across all datasets achieved an RMSE of 0.41 t ha−1, reflecting the higher reliability of module-based yield measurements. These results demonstrate the potential of Sentinel-2 VIs combined with logistic regression to predict cotton yields in the Cerrado, complementing or replacing harvester-based monitoring. Full article
(This article belongs to the Special Issue Remote Sensing for Enhanced Agricultural Crop Management)
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18 pages, 2475 KB  
Article
A Machine Learning Framework for Classifying Thermal Stress in Bean Plants Using Hyperspectral Data
by Lucas Prado Osco, Érika Akemi Saito Moriya, Bruna Coelho de Lima, Ana Paula Marques Ramos, José Marcato Júnior, Wesley Nunes Gonçalves, Lúcio André de Castro Jorge, Veraldo Liesenberg, Jonathan Li, Ademir Sérgio Ferreira de Araújo, Nilton Nobuhiro Imai and Fábio Fernando de Araújo
AgriEngineering 2025, 7(11), 376; https://doi.org/10.3390/agriengineering7110376 - 7 Nov 2025
Viewed by 672
Abstract
Rising global temperatures pose a significant threat to agricultural productivity, making the early detection of plant stress crucial for minimizing crop losses. While hyperspectral remote sensing is a powerful tool for monitoring plant health, the precise spectral regions and most effective machine learning [...] Read more.
Rising global temperatures pose a significant threat to agricultural productivity, making the early detection of plant stress crucial for minimizing crop losses. While hyperspectral remote sensing is a powerful tool for monitoring plant health, the precise spectral regions and most effective machine learning models for detecting thermal stress remain an open research question. This study presents a robust framework that utilizes eight state-of-the-art machine learning algorithms to classify the reflectance response of thermal-induced stress in two cultivars of bean plants. Our controlled experiment measured hyperspectral data across two growth stages and three stress conditions (pre-stress, during stress, and post-stress) using a spectroradiometer. The results demonstrate the high performance of several algorithms, with the Artificial Neural Network (ANN) achieving an impressive 99.4% overall accuracy. A key contribution of this work is the identification of the most contributory spectral ranges for thermal stress discrimination: the green region (530–570 nm) and the red-edge region (700–710 nm). This framework is a feasible and effective tool for modelling the hyperspectral response of thermal-stressed bean plants and provides critical guidance for future research on stress-specific spectral indices. Full article
(This article belongs to the Special Issue Remote Sensing for Enhanced Agricultural Crop Management)
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