Remote Sensing and GIS in Sustainable and Precision Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 721

Special Issue Editors


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Guest Editor
Department of Agricultural Engineering, Federal University of Ceará, Fortaleza 60455-760, CE, Brazil
Interests: water quality; remote sensing; hydrology of semiarid regions; sustainability of agricultural systems; agricultural engineering; remote sensing and watershed management

E-Mail Website
Guest Editor
Department of Agricultural Engineering, Federal University of Ceara, Fortaleza 60455-760, CE, Brazil
Interests: agricultural engineering; geoprocessing; precision agriculture; irrigation management and optimization

Special Issue Information

Dear Colleagues,

This Special Issue aims to gather cutting-edge research on the application of geotechnologies, including remote sensing, Geographic Information Systems (GIS), UAVs, LiDAR, spatial modeling, and artificial intelligence, to address the challenges of modern agriculture.

We welcome manuscripts that advance understanding and operationalization of the integrated use of these tools in the following:

  • Monitoring crop phenology and productivity;
  • Mapping the spatial and temporal variability of nutrients, water, and biotic/abiotic stresses;
  • Supporting precision agriculture practices at multiple scales, from small farms to public policy;
  • Developing data fusion methodologies (multispectral, hyperspectral, radar, UAV/LiDAR) with agricultural and climatic models;
  • Assessing environmental impacts, ecosystem services, and sustainable soil use.

The focus is both on methodological advances, such as new vegetation indices, algorithms, digital platforms, and spatial models, and on practical applications, including case studies, field validation, economic analysis, and sustainability.

Prof. Dr. Fernando Bezerra Lopes
Prof. Dr. Adunias Dos Santos Teixeira
Guest Editors

Manuscript Submission Information

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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. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Dear Colleagues,

This Special Issue aims to gather cutting-edge research on the application of geotechnologies, including remote sensing, Geographic Information Systems (GIS), UAVs, LiDAR, spatial modeling, and artificial intelligence, to address the challenges of modern agriculture.

We welcome manuscripts that advance understanding and operationalization of the integrated use of these tools in the following:

  • Monitoring crop phenology and productivity;
  • Mapping the spatial and temporal variability of nutrients, water, and biotic/abiotic stresses;
  • Supporting precision agriculture practices at multiple scales, from small farms to public policy;
  • Developing data fusion methodologies (multispectral, hyperspectral, radar, UAV/LiDAR) with agricultural and climatic models;
  • Assessing environmental impacts, ecosystem services, and sustainable soil use.

The focus is both on methodological advances, such as new vegetation indices, algorithms, digital platforms, and spatial models, and on practical applications, including case studies, field validation, economic analysis, and sustainability.

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Published Papers (1 paper)

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Research

23 pages, 8187 KB  
Article
DCFENet: A Dual-Branch Collaborative Feature Enhancement Network for Farmland Boundary Detection
by Mengyao Lan, Bangjun Huang and Peng Wu
Agronomy 2026, 16(10), 964; https://doi.org/10.3390/agronomy16100964 (registering DOI) - 12 May 2026
Viewed by 203
Abstract
Farmland resources are fundamental to human survival and play a vital role in ensuring global food security. However, farmland boundary detection remains a significant technical challenge due to the low proportion of boundary pixels, multi-scale variations, and weak boundary continuity. To address these [...] Read more.
Farmland resources are fundamental to human survival and play a vital role in ensuring global food security. However, farmland boundary detection remains a significant technical challenge due to the low proportion of boundary pixels, multi-scale variations, and weak boundary continuity. To address these issues, this study proposes DCFENet, a dual-branch collaborative feature enhancement network. Specifically, a multi-scale feature fusion attention module TA-ASPP (Task-Aware Atrous Spatial Pyramid Pooling) is designed, which effectively enhances the network’s perception of farmland boundary features by integrating multi-scale dilated convolutions with skeleton-aware attention. In addition, a dual-branch decoding structure is proposed to enhance boundary localization and global topology modeling through boundary-aware gating and cross-branch feature fusion, thereby improving the boundary continuity. Furthermore, a collaborative constraint mechanism is proposed for dual-branch decoding, which supervises the two decoders using boundary loss and skeleton loss, thereby enhancing structural consistency and topology preservation. Experimental results demonstrate that DCFENet achieves precision, recall, and boundary IoU of 74.5%, 68.1%, and 77.4%, respectively, representing an improvement of 26.8%, 36.3%, and 13.2% compared with ResNet18_UNet. It also outperforms mainstream methods such as UNet, EdgeNAT, and EDTER. In terms of computational efficiency, DCFENet contains 26.43 M parameters and 37.43 G FLOPs, with a memory usage of 1.03 GB and an inference speed of 97.97 FPS, achieving a good balance between accuracy and efficiency. The results demonstrate the efficiency and accuracy of DCFENet in extracting farmland boundaries from high-resolution remote sensing images, providing technical support for farmland management and the advancement of precision and digital agriculture. Full article
(This article belongs to the Special Issue Remote Sensing and GIS in Sustainable and Precision Agriculture)
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