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Advanced Remote Sensing for Next-Generation Smart Agriculture: Innovations, Integration, and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 23 January 2026 | Viewed by 1429

Special Issue Editors

Prof. Dr. Fenghua Yu

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Interests: RTM; crop model; UAV; crop mapping; crop model
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Yang

E-Mail Website
Guest Editor
1. National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 10089, China
2. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: orchard monitoring; crop phenotyping; LiDAR; UAV
Special Issues, Collections and Topics in MDPI journals
Dr. Qiaomin Chen

E-Mail Website
Guest Editor
School of Agriculture and Food Sustainability, Faculty of Science, The University of Queensland, Brisbane, Australia
Interests: crop modelling; plant phenotyping; machine learning; climate adaptation
Dr. Xiaobo Sun

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: precision agriculture; agricultural machinery and equipment; intelligent agriculture; intelligent agricultural equipment; crop growth monitoring; plant protection drone equipment; drone remote sensing

Special Issue Information

Dear Colleagues,

The escalating global challenges of population growth, climate change, and resource scarcity demand a paradigm shift in agricultural production towards more efficient, sustainable, and resilient systems. Smart agriculture, also known as Agriculture 4.0, has emerged as a data-driven solution with remote sensing technology at its core. Recent advancements in sensor technologies, such as hyperspectral, LiDAR, and thermal imaging, coupled with new platforms like high-resolution satellite constellations and unmanned aerial vehicles (UAVs), are generating unprecedented data streams. Simultaneously, the evolution of powerful analytical methods, including Artificial Intelligence and deep learning, is unlocking the full potential of this data to monitor and manage agricultural landscapes with remarkable precision and scale. This synergy is revolutionizing applications from field-level phenotyping to regional-scale yield forecasting. 

This Special Issue aims to curate a collection of cutting-edge research that highlights the latest innovations and applications of remote sensing in smart agriculture.  The goal is to bridge the gap between technological potential and practical on-farm implementation, creating actionable intelligence for farmers, agronomists, and policymakers.

We invite submissions of original research articles and comprehensive reviews covering, but not limited to, the following topics:

  • Deep learning and machine learning algorithms for crop classification, segmentation, and status monitoring.
  • Fusion of multi-modal remote sensing data (e.g., optical, LiDAR, SAR, thermal).
  • High-throughput phenotyping using remote and proximal sensing.
  • Estimation and forecasting of crop yield and biomass.
  • Monitoring of soil properties, including moisture, nutrients, and organic carbon.
  • Detection and management of biotic and abiotic stresses (e.g., pests, diseases, water scarcity, nutrient deficiencies).
  • Remote sensing for precision irrigation, fertilization, and spraying.
  • Development of remote sensing-driven Decision Support Systems (DSS) for agriculture.

Prof. Dr. Fenghua Yu
Dr. Hao Yang
Dr. Qiaomin Chen
Dr. Xiaobo Sun
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 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

  • smart agriculture
  • remote sensing
  • Unmanned Aerial Vehicles (UAVs)
  • satellite imagery
  • Hyperspectral & Multispectral Imaging
  • LiDAR
  • Artificial Intelligence (AI)
  • crop monitoring
  • yield prediction

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Published Papers (2 papers)

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Research

29 pages, 19475 KB  
Article
Fine-Scale Grassland Classification Using UAV-Based Multi-Sensor Image Fusion and Deep Learning
by Zhongquan Cai, Changji Wen, Lun Bao, Hongyuan Ma, Zhuoran Yan, Jiaxuan Li, Xiaohong Gao and Lingxue Yu
Remote Sens. 2025, 17(18), 3190; https://doi.org/10.3390/rs17183190 - 15 Sep 2025
Viewed by 267
Abstract
Grassland classification via remote sensing is essential for ecosystem monitoring and precision management, yet conventional satellite-based approaches are fundamentally constrained by coarse spatial resolution. To overcome this limitation, we harness high-resolution UAV multi-sensor data, integrating multi-scale image fusion with deep learning to achieve [...] Read more.
Grassland classification via remote sensing is essential for ecosystem monitoring and precision management, yet conventional satellite-based approaches are fundamentally constrained by coarse spatial resolution. To overcome this limitation, we harness high-resolution UAV multi-sensor data, integrating multi-scale image fusion with deep learning to achieve fine-scale grassland classification that satellites cannot provide. First, four categories of UAV data, including RGB, multispectral, thermal infrared, and LiDAR point cloud, were collected, and a fused image tensor consisting of 10 channels (NDVI, VCI, CHM, etc.) was constructed through orthorectification and resampling. For feature-level fusion, four deep fusion networks were designed. Among them, the MultiScale Pyramid Fusion Network, utilizing a pyramid pooling module, effectively integrated spectral and structural features, achieving optimal performance in all six image fusion evaluation metrics, including information entropy (6.84), spatial frequency (15.56), and mean gradient (12.54). Subsequently, training and validation datasets were constructed by integrating visual interpretation samples. Four backbone networks, including UNet++, DeepLabV3+, PSPNet, and FPN, were employed, and attention modules (SE, ECA, and CBAM) were introduced separately to form 12 model combinations. Results indicated that the UNet++ network combined with the SE attention module achieved the best segmentation performance on the validation set, with a mean Intersection over Union (mIoU) of 77.68%, overall accuracy (OA) of 86.98%, F1-score of 81.48%, and Kappa coefficient of 0.82. In the categories of Leymus chinensis and Puccinellia distans, producer’s accuracy (PA)/user’s accuracy (UA) reached 86.46%/82.30% and 82.40%/77.68%, respectively. Whole-image prediction validated the model’s coherent identification capability for patch boundaries. In conclusion, this study provides a systematic approach for integrating multi-source UAV remote sensing data and intelligent grassland interpretation, offering technical support for grassland ecological monitoring and resource assessment. Full article
(This article belongs to the Special Issue Advanced Remote Sensing for Next-Generation Smart Agriculture: Innovations, Integration, and Applications)
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18 pages, 3714 KB  
Article
Estimating Rice SPAD Values via Multi-Sensor Data Fusion of Multispectral and RGB Cameras Using Machine Learning with a Phenotyping Robot
by Miao Su, Weixing Cao, Shaoyang Luo, Yaze Yun, Guangzheng Zhang, Yan Zhu, Xia Yao and Dong Zhou
Remote Sens. 2025, 17(17), 3069; https://doi.org/10.3390/rs17173069 - 3 Sep 2025
Viewed by 887
Abstract
Chlorophyll is crucial for crop photosynthesis and useful for monitoring crop growth and predicting yield. Its content can be indicated by SPAD meter readings. However, SPAD-based monitoring of rice is time- and labor-intensive, whereas remote sensing offers non-destructive, rapid, real-time solutions. Compared with [...] Read more.
Chlorophyll is crucial for crop photosynthesis and useful for monitoring crop growth and predicting yield. Its content can be indicated by SPAD meter readings. However, SPAD-based monitoring of rice is time- and labor-intensive, whereas remote sensing offers non-destructive, rapid, real-time solutions. Compared with mainstream unmanned aerial vehicle, emerging phenotyping robots can carry multiple sensors and acquire higher-resolution data. Nevertheless, the feasibility of estimating rice SPAD using multi-sensor data obtained by phenotyping robots remains unknown, and whether the integration of machine learning algorithms can improve the accuracy of rice SPAD monitoring also requires investigation. This study utilizes phenotyping robots to acquire multispectral and RGB images of rice across multiple growth stages, while simultaneously collecting SPAD values. Subsequently, four machine learning algorithms—random forest, partial least squares regression, extreme gradient boosting, and boosted regression trees—are employed to construct SPAD monitoring models with different features. The random forest model combining vegetation indices, color indices, and texture features achieved the highest accuracy (R2 = 0.83, RMSE = 1.593). In summary, integrating phenotyping robot-derived multi-sensor data with machine learning enables high-precision, efficient, and non-destructive rice SPAD estimation, providing technical and theoretical support for rice phenotyping and precision cultivation. Full article
(This article belongs to the Special Issue Advanced Remote Sensing for Next-Generation Smart Agriculture: Innovations, Integration, and Applications)
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