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Agriculture
Special Issues
Remote Sensing in Crop Protection
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Remote Sensing in Crop Protection

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1886

Special Issue Editors

Prof. Dr. Boris Evstatiev

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Guest Editor
Faculty of Еlectrical Engineering, Electronics and Automation, University of Ruse, Ruse 7004, Bulgaria
Interests: Internet of Things (IoT); sensors; electronics; information and communication technologies (ICT); data analysis; deep learning; classification; clustering analysis; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Atanas Atanasov

E-Mail Website
Guest Editor
Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, Ruse 7004, Bulgaria
Interests: modern agriculture technologies; smart greenhouses; smart vegetable growing; crop monitoring; precision farming; farm automation; remote sensing; data-driven farming
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Comparetti

E-Mail Website
Guest Editor
Department of Agricultural, Food and Forest Sciences, University of Palermo, Viale delle Scienze, Building 4, 90128 Palermo, Italy
Interests: precision agriculture; Global Navigation Satellite Systems (GNSS) for agricultural machines; geo-referenced measurement and mapping of soil compaction; remote sensing; renewable energy in agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of remote sensing technologies and artificial intelligence has opened many new opportunities for the precise monitoring and control of crop production. The combined application of drones, satellites, different spectrum sensors, machine learning, and deep learning enables the optimization of crop protection by introducing the early identification of pests and diseases, smart application of pesticides, etc. Together, these innovations support higher yields, improved resource efficiency, and more resilient farming systems.

This Special Issue invites contributions that explore the application of the abovementioned technologies in all areas of crop protection. We welcome original research articles, reviews, and case studies on (but not limited to) the following:

  • Remote sensing for crop protection;
  • Machine learning- and deep learning-enhanced crop protection;
  • Early identification of pests and diseases;
  • Analysis of UAV-obtained and satellite-obtained data;
  • Pest identification-based on vegetation indices;
  • Cloud computing and decision support systems for smart crop protection.

We encourage researchers and practitioners to share their latest findings, innovations, and practical applications that drive the digital transformation and sustainability of crop protection.

Prof. Dr. Boris Evstatiev
Dr. Atanas Atanasov
Dr. Antonio Comparetti 
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. Agriculture is an international peer-reviewed open access semimonthly 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 2600 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

  • satellite
  • drone
  • unmanned aerial vehicles (UAVs)
  • machine learning
  • deep learning
  • artificial intelligence (AI)
  • remote sensing
  • cloud computing
  • information systems
  • crop protection
  • vegetation indices

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

Published Papers (2 papers)

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Research

23 pages, 18619 KB  
Article
Monitoring Sitobion avenae Infestations in Winter Wheat Using UAV-Obtained RGB Images and Deep Learning
by Atanas Z. Atanasov, Boris I. Evstatiev, Asparuh I. Atanasov, Plamena D. Nikolova and Antonio Comparetti
Agriculture 2026, 16(6), 640; https://doi.org/10.3390/agriculture16060640 - 11 Mar 2026
Viewed by 555
Abstract
The grain aphid (Sitobion avenae) is a major pest of winter wheat, causing significant yield losses through direct feeding and as a vector of barley yellow dwarf virus (BYDV). Populations can increase rapidly under moderate temperatures and low rainfall, potentially leading [...] Read more.
The grain aphid (Sitobion avenae) is a major pest of winter wheat, causing significant yield losses through direct feeding and as a vector of barley yellow dwarf virus (BYDV). Populations can increase rapidly under moderate temperatures and low rainfall, potentially leading to severe infestations if not effectively monitored and managed. This study develops and validates a UAV-based RGB imaging methodology, which relies on deep learning for accurate detection and assessment of Sitobion avenae in wheat crops. The RGB images are preliminarily filtered using “histogram equalization”, which allows for highlighting the infested areas. An experimental study was conducted under the specific climatic conditions of Southern Dobruja, Bulgaria, to quantify Sitobion avenae infestations. Three neural network architectures were used (DeepLabv3, U-Net, and PSPNet) in combination with three backbone models: ResNet34, ResNet50, and ResNet101. The optimal combination was determined to be the U-Net + ResNet101 model, which achieved an average F1 score of 0.982 and a Cohen’s Kappa coefficient of 0.966. The results demonstrate that UAV-based detection allows precise mapping of infested areas, enabling targeted insecticide applications and effective pest management while substantially reducing chemical inputs. These findings indicate that the proposed framework provides a reliable and scalable tool for precision pest monitoring and control in winter wheat. Full article
(This article belongs to the Special Issue Remote Sensing in Crop Protection)
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27 pages, 13622 KB  
Article
Deep Learning Improves Planting Year Estimation of Macadamia Orchards in Australia
by Andrew Clark, James Brinkhoff, Andrew Robson and Craig Shephard
Agriculture 2025, 15(22), 2346; https://doi.org/10.3390/agriculture15222346 - 11 Nov 2025
Viewed by 877
Abstract
Deep learning reduced macadamia planting year error at a national scale, achieving a pixel-level Mean Absolute Error (MAE) of 1.2 years and outperforming a vegetation index threshold baseline (MAE 1.6 years) and tree-based models—Random Forest (RF; MAE 3.02 years) and Gradient Boosted Trees [...] Read more.
Deep learning reduced macadamia planting year error at a national scale, achieving a pixel-level Mean Absolute Error (MAE) of 1.2 years and outperforming a vegetation index threshold baseline (MAE 1.6 years) and tree-based models—Random Forest (RF; MAE 3.02 years) and Gradient Boosted Trees (GBT; MAE 2.9 years). Using Digital Earth Australia Landsat annual geomedians (1988–2023) and block-level, industry-supplied planting year data, models were trained and evaluated at the pixel level under a strict Leave-One-Region-Out cross-validation (LOROCV) protocol; a secondary block-level random split (80/10/10) is reported only to illustrate the more optimistic setting, where shared regional conditions yield lower errors (0.6–0.7 years). Predictions reconstruct planting year retrospectively from the full historical record rather than providing real-time forecasts. The final model was then applied to all Australian Tree Crop Map (ATCM) macadamia orchard polygons to produce wall-to-wall planting year estimates. The approach enables fine-grained mapping of planting patterns to support yield forecasting, resource allocation, and industry planning. Results indicate that sequence-based deep models capture informative temporal dynamics beyond thresholding and conventional machine learning baselines, while remaining constrained by regional and temporal data sparsity. The framework is scalable and transferable, offering a pathway to planting year mapping for other perennial crops and to more resilient, data-driven agricultural decision-making. Full article
(This article belongs to the Special Issue Remote Sensing in Crop Protection)
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