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Remote Sensing
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Remote Sensing and Associated Artificial Intelligence in Agricultural Applications (2nd...
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Remote Sensing and Associated Artificial Intelligence in Agricultural Applications (2nd Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1671

Special Issue Editors

Prof. Dr. Enrico Corrado Borgogno Mondino

E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, Italy
Interests: remote sensing; spatial analysis and landscape planning; GIS; digital photogrammetry; precision farming; lidar
Special Issues, Collections and Topics in MDPI journals
Dr. Filippo Sarvia

E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, Grugliasco 10095, Italy
Interests: remote sensing; Earth Observation data; agronomy; classification; AI; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Samuele De Petris

E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences, University of Turin, L.go Braccini 2, 10095 Grugliasco, Italy
Interests: remote sensing; earth observation data; forestry; ecology; GIS; photogrammetry; statistics; fire
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on “Remote Sensing and Associated Artificial Intelligence in Agricultural Applications (2nd Edition)” aims to assemble high-level contributions to provide an exhaustive overview of ongoing geomatics and remote sensing-related techniques, making use of artificial intelligence to support agricultural applications.

The following topics are strongly encouraged:

  • Research experiences relating to the potentialities and limits of AI in supporting remote sensing-based applications in agricultural and forest contexts. Special focuses placed on the comparison between AI-based and traditional approaches is highly desirable, with the aim of pointing out whether, when and where AI can be successfully and undoubtedly used in place of more ordinary and explicit approaches.
  • AI for data integration aimed at maximizing the exploitation of spatial, temporal and spectral features of sensors from different platforms with special concern about scalable approaches relying of the adoption of RPAS, aerial and satellite datasets.
  • AI for supporting remote sensing-based services in agriculture and its relationship with data integration and analysis systems (DIASs), high-performance computing (HPC) and Internet of Things (IoT).
  • AI for hyper/multi-spectral image interpretation/classification.
  • AI for point cloud interpretation from digital photogrammetry and LiDAR systems.
  • AI for time trends analysis and interpretation (e.g., crop phenology detection and forecasting, drought trend modeling, etc.).
  • AI to support decision-support systems for crop management (irrigation, fertilization, crop protections, etc.) based on the integration of satellite, meteorological and field data.
  • Economic analyses of future trends in the technology transfer process of AI towards the agricultural sector, scenarios of profit and reports about the feelings of farmers regarding AI introduction in their ordinary workflow.

All other proposals related to the adoption of AI applied to remote sensing in agriculture will also be evaluated.

Prof. Dr. Enrico Corrado Borgogno Mondino
Dr. Filippo Sarvia
Dr. Samuele De Petris
Guest Editors

Dr. Alessandro Farbo
Guest Editor Assistant

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Remote Sensing 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 2700 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

  • remote sensing
  • agricultural application
  • common agricultural policy (CAP)
  • precision agriculture
  • machine learning
  • service prototype development
  • crop monitoring
  • crop detection
  • deep learning
  • artificial intelligence
  • crop classification
  • yield prediction
  • GIS application
  • precision farming
  • remotely piloted aircraft systems (RPASs)
  • image segmentation

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

Related Special Issue

Published Papers (2 papers)

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Research

26 pages, 16947 KiB  
Article
Optimization Simulation and Comprehensive Evaluation Coupled with CNN-LSTM and PLUS for Multi-Scenario Land Use in Cultivated Land Reserve Resource Area
by Shaner Li, Chao Zhang, Chang Chen, Cuicui Yang, Lihua Zhao and Xuechuan Bai
Remote Sens. 2025, 17(9), 1619; https://doi.org/10.3390/rs17091619 - 2 May 2025
Viewed by 485
Abstract
The scientific development and utilization of cultivated land reserve resource areas is an important basis for realizing national food security and regional ecological protection. This paper focuses on land use optimization simulations to explore the paths of sustainable land use in cultivated land [...] Read more.
The scientific development and utilization of cultivated land reserve resource areas is an important basis for realizing national food security and regional ecological protection. This paper focuses on land use optimization simulations to explore the paths of sustainable land use in cultivated land reserve resources areas. Deep learning technology was introduced to calculate the growth probability of each land use type. A land use change simulation method coupling CNN-LSTM and PLUS was constructed to dynamically simulate the land use pattern, and the spatial accuracy of the simulation was improved. Markov chains and multi-objective planning (MOP) model were used to set historical development (HD) scenarios, ecological conservation (EP) scenarios, land consolidation (LC) scenarios, and sustainable development (SD) scenarios. The comprehensive impact of land use change on ecosystem service value (ESV), agricultural production benefits (APBs), and carbon balance (CB) was evaluated by systematically analyzing the quantitative and spatial distribution characteristics of land use change in different scenarios from 2020 to 2030. Da’an City, Jilin province, China was selected as the study area. The results of this study show the following: (1) The CNN-LSTM coupled with the PLUS model was designed to capture the dynamic change characteristics of land use, which achieves high accuracy (Kappa of 0.8119). (2) In the EP scenario, the increase in ESV was 4.36%, but the increase in APB was only 7.33%. In the LC scenario, APB increased by 22.11%, while ESV decreased by 3.44%. In the SD scenario, a dynamic balance was achieved between ESV and APB, and it was the optimal path for sustainable development. (3) The SD scenario performed best, with a CB of 5,532,100 tons, while the EP scenario was the lowest, at only 1,493,500 tons. The SD scenario shows the optimal potential of combining carbon reduction and agricultural development. In this paper, deep learning and spatial modeling for multi-scenario simulation were integrated, and a scientific basis for the planning and management of cultivated land reserve resource areas was provided. Full article
(This article belongs to the Special Issue Remote Sensing and Associated Artificial Intelligence in Agricultural Applications (2nd Edition))
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32 pages, 13599 KiB  
Article
Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
by Sam Khallaghi, Rahebeh Abedi, Hanan Abou Ali, Hamed Alemohammad, Mary Dziedzorm Asipunu, Ismail Alatise, Nguyen Ha, Boka Luo, Cat Mai, Lei Song, Amos Olertey Wussah, Sitian Xiong, Yao-Ting Yao, Qi Zhang and Lyndon D. Estes
Remote Sens. 2025, 17(3), 474; https://doi.org/10.3390/rs17030474 - 30 Jan 2025
Cited by 1 | Viewed by 825
Abstract
Mapping agricultural fields using high-resolution satellite imagery and deep learning (DL) models has advanced significantly, even in regions with small, irregularly shaped fields. However, effective DL models often require large, expensive labeled datasets, which are typically limited to specific years or regions. This [...] Read more.
Mapping agricultural fields using high-resolution satellite imagery and deep learning (DL) models has advanced significantly, even in regions with small, irregularly shaped fields. However, effective DL models often require large, expensive labeled datasets, which are typically limited to specific years or regions. This restricts the ability to create annual maps needed for agricultural monitoring, as changes in farming practices and environmental conditions cause domain shifts between years and locations. To address this, we focused on improving model generalization without relying on yearly labels through a holistic approach that integrates several techniques, including an area-based loss function, Tversky-focal loss (TFL), data augmentation, and the use of regularization techniques like dropout. Photometric augmentations helped encode invariance to brightness changes but also increased the incidence of false positives. The best results were achieved by combining photometric augmentation, TFL, and Monte Carlo dropout, although dropout alone led to more false negatives. Input normalization also played a key role, with the best results obtained when normalization statistics were calculated locally (per chip) across all bands. Our U-Net-based workflow successfully generated multi-year crop maps over large areas, outperforming the base model without photometric augmentation or MC-dropout by 17 IoU points. Full article
(This article belongs to the Special Issue Remote Sensing and Associated Artificial Intelligence in Agricultural Applications (2nd Edition))
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