Application of Remote Sensing and Machine Learning in Precision Agriculture
A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".
Deadline for manuscript submissions: 15 April 2027 | Viewed by 991
Special Issue Editors
Interests: precision agriculture; machine learning; remote sensing
Interests: digital mechanization; precision agriculture; smart harvesting systems
Special Issues, Collections and Topics in MDPI journals
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-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 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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