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

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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: 14 January 2027 | Viewed by 9799

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Editors

Prof. Dr. Fenghua Yu

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College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Interests: RTM; crop model; UAV; crop mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Yang

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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

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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 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-anonymized 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 (6 papers)

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Research

25 pages, 10987 KB

Open AccessArticle

Trait-Specific Contributions of UAV Multispectral, RGB and Structural Features to Soybean SPAD and Plant Height Phenotyping

by Qing Li, Dalei Hao, Wenfeng Liu, Renan Caldas Umburanas and Yelu Zeng

Remote Sens. 2026, 18(15), 2642; https://doi.org/10.3390/rs18152642 - 6 Aug 2026

Viewed by 269

Abstract

Unmanned aerial vehicle (UAV) imagery can support plot-scale crop phenotyping, but spectral, RGB and structural predictors may contribute differently to different traits. We compared six predefined feature groups for predicting soybean SPAD and plant height (PH) in a 1.3 ha field experiment in Sanya, China. The field contained 6197 soybean planting plots, of which 234 had paired SPAD and PH measurements. Multispectral bands, vegetation indices (VIs), RGB descriptors and digital surface model (DSM) metrics were extracted from DJI Mavic 3 Multispectral imagery. Six regression algorithms were evaluated using random fivefold cross-validation, spatial block cross-validation and nested spatial cross-validation. Under random cross-validation, ExtraTrees with multispectral bands, VIs and RGB descriptors produced the numerically highest SPAD performance (R² = 0.589; RMSE = 6.66), while BayesianRidge with multispectral bands, VIs and DSM metrics produced the highest PH performance (R² = 0.760; RMSE = 7.14 cm). Nested spatial cross-validation yielded R² = 0.473 and RMSE = 7.56 for SPAD and R² = 0.690 and RMSE = 8.13 cm for PH. G4 was selected in four of the five outer folds for SPAD, although the selected algorithm varied, and G5 was selected in all five outer folds for PH. VIs improved prediction of both traits relative to the original bands. Adding RGB descriptors produced only a small and model-dependent improvement for SPAD, whereas adding DSM metrics produced a larger and more consistent improvement for PH. The complete feature set did not outperform G4 for SPAD or G5 for PH. The retained models were applied to all 6197 plots to map SPAD, PH and their field relative combinations. Because all of the validations used one field and one UAV acquisition date, the results describe performance within this experiment and do not establish transferability to other sites, years or growth stages. 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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36 pages, 10946 KB

Open AccessArticle

Predicting Tart Cherry Stem Water Potential Using UAV Multispectral Imagery and Environmental Data via Symbolic Regression

by Anderson L. S. Safre, Alfonso Torres-Rua, Kurt Wedegaertner, Brent Black, Brennan Bean, Burdette Barker and Matt Yost

Remote Sens. 2026, 18(6), 853; https://doi.org/10.3390/rs18060853 - 10 Mar 2026

Viewed by 781

Abstract

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). Six equations were generated for different data-availability scenarios and validated using a leave-one-tree-out (modified k-fold) approach, resulting in Ψ_stem estimates with R² values ranging from 0.67 to 0.80 and root mean square errors (RMSE) ranging from 0.11 to 0.08 MPa. Notably, SR was able to produce interpretable equations that enhance model transparency and transferability. Model robustness was further confirmed using an independent dataset from a different location. To our knowledge, this is the first application of SR for Ψ_stem estimation, offering a scalable and interpretable tool to support irrigation management in tart cherry orchards. 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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24 pages, 9283 KB

Open AccessArticle

High-Precision Crop Identification and Feature Contribution Mechanism in Plateau Mountainous Regions Based on Adaptive Geographic Partitioning and Local Modeling

by Guoping Chen, Zhao Song, Junsan Zhao, Yandong Wang, Changman Wang, Weihai Li and Yanying Wang

Remote Sens. 2026, 18(5), 709; https://doi.org/10.3390/rs18050709 - 27 Feb 2026

Viewed by 635

Abstract

Accurate crop identification in plateau mountainous regions is essential for food security, yet geospatial non-stationarity and topography-induced spectral paradoxes often compromise global model performance due to a “homogenization constraint.” This study developed an adaptive local modeling framework that partitions the landscape into biophysically homogeneous subregions using the Spectral Angle Mapper (SAM), effectively isolating terrain-induced illumination variance from intrinsic spectral responses. Independent local classifiers were coupled with SHAP and GAMs to interpret the resulting spatial variations in feature contributions. Results demonstrate that: (1) the partitioned local models significantly outperform the global baseline (OA = 93.1%, Kappa = 0.919) by mitigating the suppression of local signals inherent in global datasets; (2) the framework captures a mechanistic shift in feature importance, transitioning from a strong “Hydro-Topographic” coupling in highlands (Interaction Strength > 0.15) to “Spectral-Texture” complementarity in plains; and (3) major crop distributions are governed by quantifiable biophysical thresholds—such as a <28.5 °C thermal limit for maize and a >971 mm precipitation boundary for rice—which exhibit consistency with regional agrometeorological principles. These findings suggest that integrating adaptive partitioning with interpretable local modeling transforms geospatial non-stationarity from a source of classification error into explicit, zone-specific decision rules, providing a robust and scientifically grounded solution for precision agriculture in heterogeneous terrains. 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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29 pages, 9665 KB

Open AccessArticle

Gully Extraction in Northeast China’s Black Soil Region: A Multi-CNN Comparison with Texture-Enhanced Remote Sensing

by Jiaxin Yu, Jiuchun Yang, Xiaoyan Xu and Liwei Ke

Remote Sens. 2025, 17(23), 3792; https://doi.org/10.3390/rs17233792 - 21 Nov 2025

Cited by 4 | Viewed by 1601

Abstract

Gully erosion poses a serious threat to soil fertility and agricultural sustainability in Northeast China’s black soil region. Accurate and efficient mapping of erosion gullies is critical for enabling targeted soil conservation and precision land management. In this study, we developed a texture-enhanced deep learning framework for automated gully extraction using high-resolution GF-1 and GF-2 satellite imagery. Key texture parameters—specifically mean and contrast features derived from the gray-level co-occurrence matrix (GLCM) under a 5 × 5 window and 32 gray levels—were systematically optimized and fused with multispectral bands. We trained and evaluated three convolutional neural network architectures—U-Net, U-Net++, and DeepLabv3+—under consistent data and evaluation protocols. Results demonstrate that the integration of texture features significantly enhanced extraction performance, with U-Net achieving the highest overall accuracy (90.27%) and average precision (90.87%), surpassing DeepLabv3+ and U-Net++ by margins of 6.06% and 9.33%, respectively. Visualization via Class Activation Mapping (CAM) further confirmed improved boundary discrimination and reduced misclassification of spectrally similar non-gully features, such as field roads and farmland edges. The proposed GLCM–CNN integrated approach offers an interpretable and transferable solution for gully identification and provides a technical foundation for large-scale monitoring of soil and water conservation in black soil landscapes. 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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29 pages, 19475 KB

Open AccessArticle

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

Cited by 6 | Viewed by 2788

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 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

Open AccessArticle

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

Cited by 8 | Viewed by 2488

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 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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