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: 30 March 2026 | Viewed by 621

Special Issue Editors


E-Mail Website
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

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

E-Mail Website
Guest Editor
Department of Agricultural, Food and Forest Sciences (SAAF), 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

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 455
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)
Show Figures

Figure 1

Back to TopTop