Search Results (2,337)

As a core resource of traditional Chinese medicine (TCM), medicinal plants are conventionally monitored and assessed using high-cost, low-efficiency methods. Remote sensing offers an efficient technical alternative for large-scale and dynamic evaluation. This study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods, and specifically analyzed their applications in medicinal plant resource investigation, planting monitoring, stress monitoring, and TCM quality assessment. These studies mainly focus on resource surveys and quality analysis, targeting root and rhizome herbs. Integrated satellite-, UAV-, and ground-based remote sensing enables distribution mapping, growth retrieval, stress monitoring, and non-destructive quality evaluation in medicinal plants, achieving overall accuracies ranging from 80% to 100%. Currently, remote sensing applications in medicinal plants are evolving toward space–air–ground integration, multi-source data fusion, artificial intelligence empowerment, and multi-omics integration. However, they are constrained by complex wild habitats, difficulties in monitoring root herbs, spectral confusion, and limited model generalization. Future efforts should focus on establishing an integrated monitoring network, developing full-chain quality inversion models for geo-authentic herbs, building climate-adaptive cultivation systems, creating early pest–disease warning technologies, and deepening the integration of remote sensing and multi-omics to support the sustainable utilization and high-quality development of medicinal plant resources. Full article

Monitoring avocado (Persea americana Mill., cv. Semil-34) in tropical mountain landscapes of Cambita, San Cristóbal, Dominican Republic is inherently complex due to the pronounced topographical and climatic heterogeneity that modulates the crop’s ecophysiological responses, specifically vegetative vigor, carbon allocation, and the synchronization of reproductive flushes. This study integrates 5-year (2020–2025) Sentinel-2 time series, ERA5-Land climatic variables (air temperature, total precipitation, and radiation), and geomorphometric covariates to explain variability in yield and fruit quality. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Red Edge (NDRE), and Normalized Difference Moisture Index (NDMI), were analyzed using Partial Least Squares Regression (PLSR) to characterize phenological dynamics and rank dominant predictors. The results revealed coherent spectral phenological trajectories; however, a significant inverse relationship was detected between canopy vigor and yield during reproductive phases. High vegetation index values were significantly and negatively associated with lower production (r = −0.58, p < 0.0021), reflecting a potential source–sink imbalance. Topography functioned as a structural filter, regulating root drainage and productive stability across the landscape. While yield variability was partially explainable (R² = 0.38), internal fruit quality, measured as dry matter content, exhibited comparatively high environmental stability. A central contribution of this research lies in identifying the “vigor paradox” in cv. Semil-34 and the suggestion that topography may exert a stronger influence than direct spectral signals under tropical hillside conditions. These findings provide an exploratory framework for anticipating yield and fruit quality through satellite remote sensing or UAVs, supporting site-specific management decisions in mountain agricultural systems. Full article

This review provides a comprehensive bibliometric analysis of the literature on the integration of remote sensing data and machine learning or deep learning algorithms in precision agriculture. The analysis covers 1056 publications, included in the Web of Science Core Collection, and identifies the temporal patterns of research, the most frequently used algorithms, the prominent remote sensing technologies, and the geographical distribution of research output. Increased research output during the period of 2013–2025 is attributed to the availability of high-level computing, satellites, and UAV imagery. The earlier studies in machine learning primarily involved the use of the Random Forest and Support Vector Machine algorithms, whereas in the past few years, deep learning, and especially Convolutional Neural Networks, have become more dominant. The most widely used data sources in remote sensing are the imagery from UAVs and the Sentinel satellite missions. The evaluation revealed that most of the geographical research activity was centered in the United States and China, but there is a trend of increasing research activity in most of the other developed countries. Research in Africa and South America remains particularly underdeveloped. Considering the rapid development of research, data fusion of optical and radar satellite imagery, UAV imagery, weather and soil datasets are expected to further improve the representation of agricultural systems. Full article

Generally, land seismic data acquisition in arid areas is a labor-intensive, costly, and challenging process, often hindered by challenging terrain and safety risks. To overcome these limitations, we propose the integration of autonomous Unmanned Aerial Vehicles (UAVs) into land seismic data acquisition, enabling efficient data collection in difficult, inaccessible terrain. This is a cooperative mission workflow combining a Scouting UAV for high-resolution aerial scouting, followed by the swarm deployment of an Autonomous Seismic Acquisition Device (ASAD) for seismic data recording. The cooperative system allows for precise landing and subsequent deployment of seismic sensors in optimal locations. Previously, we demonstrated the applicability of passive seismic recorded with ASAD drones to near-surface characterization. This study covers the results of a field trial, where both the ASAD and Scouting UAV systems successfully acquired high-resolution seismic data with an active source, comparable to that of a conventional seismic data acquisition system. The results show that the ASAD seismic data exhibit a slightly higher noise level due to coupling variances and the fact that geophones were hardwired into 9-sensor arrays. However, due to its single-point sensing nature, it yields a superior frequency bandwidth, making it suitable for imaging shallow anomalies. The system underwent P-wave refraction tomography modeling and accurately detected a shallow subsurface cavity, showcasing its potential for near-surface characterization and shallow geohazard identification. This heterogeneous robotic system can support seismic data acquisition by enhancing safety, improving efficiency, and streamlining equipment mobilization, while minimizing environmental footprint. Full article

Mountainous Mediterranean rivers provide essential ecosystem services but are increasingly affected by land-use change, hydraulic works, and inadequate wastewater management. This study investigates the links between geomorphological transformation and river water quality in the Central Eurytania drainage basin (Greece) over the past two decades, within the institutional framework of European and Greek environmental legislation, with emphasis on the protection and restoration of aquatic ecosystems. Georeferenced satellite imagery from 2003/2010 and 2023, Google Earth Engine (GEE, Python Earth Engine API: 1.7.20)-based spatial analysis, high-resolution UAV orthomosaics, and seasonal spectrophotometric analyses were integrated to assess spatial and temporal dynamics. Results indicate that land-use changes, including the construction of solar parks, expansion of tourism infrastructure, and partial agricultural abandonment, reflect ongoing socio-economic shifts influencing fluvial processes. Water-quality analyses further showed that channel alteration and wastewater inputs jointly degrade ecological conditions. The findings highlight the need for integrated watershed management focused on riparian buffer restoration, improved wastewater control, and systematic monitoring of hydromorphological change. The proposed interdisciplinary framework contributes to the assessment of environmental resilience in Mediterranean mountainous watersheds, which are increasingly vulnerable to climatic and socio-economic pressures. Full article

Soil moisture content (SMC) is a critical factor in agricultural management; however, traditional monitoring methods face limitations regarding spatial resolution and the acquisition of regional dynamics. Unmanned Aerial Vehicle (UAV) remote sensing offers new opportunities for precision monitoring. This study proposes a UAV-based multi-modal remote sensing method for soil moisture estimation. Specifically, novel dual-band and three-band hyperspectral (HS) indices were constructed, and visible (RGB) and thermal infrared (TIR) information were integrated to form a multi-modal data system; simultaneously, multi-modal estimation models were developed by combining four AutoML methods: TPOT, AutoGluon, H2O AutoML, and FLAML. The results indicate that the H2O AutoML model, fusing multi-modal data, exhibited the best performance in estimating soil moisture at depths of 0–20 cm and 20–40 cm (R ≥ 0.72, RMSE 1.99–2.17%), demonstrating superior stability and generalization capabilities compared to other models. This study has made progress in hyperspectral index construction, multi-modal fusion, and soil moisture retrieval, providing a new technical approach for the refined management of agricultural water resources. Full article

Automated pavement distress classification using high-resolution Unmanned Aerial Vehicle (UAV) imagery is pivotal for intelligent transportation systems. However, long-term UAV monitoring faces a continuous stream of evolving distress types and changing remote sensing background textures, necessitating Class-Incremental Learning (CIL) capabilities. Existing methods struggle to balance stability and plasticity, especially under the severe storage limitations typical of local edge stations in air–ground collaborative systems. This data scarcity leads to catastrophic forgetting and confusion among fine-grained distress categories. To address these challenges, we propose a data-efficient approach named Analytic Geometric Alignment (AGA). Our framework mainly consists of three key components. First, to overcome the optimization gap between the feature extractor and the fixed geometric target, we introduce a Subspace-Aware Analytic Initialization (SAI) that computes a closed-form projection to instantly align the feature subspace with the ETF manifold before each task training. Second, on this aligned basis, a Decoupled Geometric Adapter (DGA) is incorporated to facilitate continuous non-linear adaptation to complex aerial textures. Finally, for stable incremental training, we design a Memory-Prioritized Regression (MPR) loss to enforce tighter geometric constraints on replay samples, significantly enhancing model stability. Extensive experiments on the UAV-PDD2023 dataset demonstrate that AGA significantly outperforms state-of-the-art methods, showcasing excellent robustness and data efficiency. Full article

Unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) has been increasingly utilized for fine-scale surface characterization and quantitative retrieval due to its capability of capturing dense spectral information at ultra-high spatial resolution. However, UAV-HSI analysis remains challenging due to high dimensionality, noise and within-class variability, as well as limited cross-flight consistency under varying acquisition conditions. Deep learning (DL) has therefore attracted growing attention by enabling spectral-spatial representation learning and more robust inference under residual degradations and domain shifts. This review summarizes DL approaches for UAV-HSI analytics and organizes the literature along a complete workflow, from imaging principles, preprocessing, and correction to DL architectures, core tasks, and representative applications, to provide guidance for future research and applications. The reviewed papers demonstrate that DL exhibits great potential and a promising future in UAV-HSI analysis. Full article

Monitoring thermal effluent is critical for assessing aquatic ecosystem health, yet traditional satellite remote sensing and in situ point measurements often fail to capture fine-scale thermal dynamics in narrow streams and complex coastal areas due to spatiotemporal resolution limitations. This study establishes a high-precision surface water temperature mapping protocol using a low-cost Unmanned Aerial Vehicle (UAV) equipped with an uncooled thermal infrared sensor (FLIR Vue Pro R) to overcome these observational gaps. We investigated two distinct hydrological environments—an inland stream and a coastal sea—to provide initial evidence for the applicability of an in situ-based linear regression calibration model across contrasting aquatic settings. The initial uncalibrated radiometric temperatures exhibited significant bias errors reaching up to 9.2 °C in the stream and 9.4 °C in the coastal area, primarily driven by atmospheric attenuation and environmental factors. However, the proposed calibration method dramatically reduced these discrepancies, achieving Root Mean Square Errors (RMSE) of 0.43 °C and 0.42 °C, respectively, with high determination coefficients (R² > 0.87). The derived high-resolution thermal maps successfully visualized the detailed diffusion patterns of thermal plumes, revealing a steep temperature gradient of approximately 13 °C in the stream discharge zone and a distinct 5 °C elevation in the coastal effluent area relative to the ambient water. These findings demonstrate that UAV-based thermal remote sensing, when coupled with a rigorous radiometric calibration strategy, can serve as a cost-effective and reliable tool for environmental monitoring, bridging the critical scale gap between local point measurements and regional satellite observations. Full article

Early detection of agricultural fires with Unmanned Aerial Vehicles (UAVs) is important for environmental safety, yet it remains difficult because ignition cues are extremely small, smoke patterns vary widely, and farmland scenes often contain strong background interference such as specular reflections. Model development is further constrained by the scarcity of data from the early ignition stage. To address these challenges, we propose a joint data and model optimization framework. We first build a hybrid dataset through an ROI-guided synthesis pipeline, in which latent diffusion models are used to insert high-fidelity, carefully screened fire samples into real farmland backgrounds. We then introduce EF-YOLO, a detector designed for high sensitivity to small targets. The network uses SPD-Conv to reduce feature loss during spatial downsampling and includes a high-resolution P2 head to improve the detection of minute objects. To reduce background clutter, a Dual-Path Frequency–Spatial Enhancement (DP-FSE) module serves as a lightweight statistical surrogate that extracts global contextual cues and local salient features in parallel, thereby suppressing high-frequency noise. Experimental results show that EF-YOLO achieves an AP_s of 40.2% on sub-pixel targets, exceeding the YOLOv8s baseline by 15.4 percentage points. With a recall of 88.7% and a real-time inference speed of 78 FPS, the proposed framework offers a strong balance between detection performance and efficiency, making it well suited for edge-deployed agricultural fire early-warning systems. Full article

The use of biostimulants in agriculture is increasing; however, their effects on raspberry remain insufficiently understood. The aim of this study was to evaluate the impact of foliar-applied biostimulants on yield and growth in three primocane raspberry cultivars grown under field conditions using multispectral imaging based on unmanned aerial vehicles. An experiment included a control and four foliar biostimulant treatments based on animal-derived amino acids, plant-derived amino acids, seaweed extract, and seaweed extract combined with animal-derived amino acids. Biostimulant effects on primocane raspberry were found to vary substantially depending on cultivar, environmental conditions, and formulation type, with measurable impacts on both yield formation and vegetative growth. These responses were further supported and characterized using multispectral UAV-based mutlispectral imaging, which enabled effective detection of treatment-related physiological changes. This approach was based on the analysis of relative percentage changes between consecutive measurements of selected vegetation indices, allowing the identification of dynamic physiological responses over time. These findings highlight the need for a more targeted approach to biostimulant use, taking into account cultivar-specific responses and environmental variability. Future research should extend this framework to a broader range of genotypes, cultivation systems, and biostimulant formulations, while integrating remote sensing with other analytical methods to better understand plant physiological responses. Such developments may support the transition toward data-driven and precision-guided biostimulant application strategies in sustainable crop production. Full article

Understanding crop water use is essential for improving agricultural water management and ensuring sustainable food production, especially in regions with limited water resources. Evapotranspiration (ET) is a key component of the hydrological cycle, directly influencing irrigation planning and crop productivity. However, accurately estimating ET at local scales remains a challenge due to the limitations of conventional measurement methods and the difficulty of integrating high-resolution remote sensing data. This study investigates the estimation of terrestrial evapotranspiration (ET) in a sugarcane cultivation area located in the northern coastal region of Paraíba, Brazil, using meteorological data and aerial images acquired by an Unmanned Aerial Vehicle (UAV). We adapted the PT-JPL model to estimate ET at the local scale, using thermal and multispectral imagery obtained from UAVs. Data validation was performed using surface energy balance measurements obtained from a micrometeorological tower, thereby enabling comparison of estimated and observed ET values. The results demonstrated strong correlations between modeled predictions and field measurements of net radiation (R² = 0.85), with performance metrics indicating moderate reliability for local-scale simulated ET when compared to flux-tower-based ET (R² = 0.48; RMSE ≈ 0.045 mm/30 min). This research highlights the potential of integrating UAV-based remote sensing with the PT-JPL model to improve understanding of crop water use, support irrigation management, and contribute to sustainable agricultural practices. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

We investigate wheat lodging segmentation from UAV RGB imagery acquired over real production fields rather than controlled experimental sites. Besides pixel-level accuracy, our evaluation also emphasizes robustness under heterogeneous farmland conditions and deployment-oriented efficiency. We propose CASGNet, an edge-oriented segmentation network with a content-aware spatial gating mechanism that reweights intermediate features according to local structural variation. Instead of uniformly aggregating features, the module suppresses responses in homogeneous regions while preserving activation in structurally complex areas. In practice, this improves the continuity of irregular lodging shapes and reduces spurious responses in relatively homogeneous backgrounds. The dataset spans 46 farms across Jiaozuo, Jiyuan, and Luoyang, covering progressively fragmented farmland. Under a stricter mission-level data-isolation protocol, CASGNet achieves 94.4% mIoU and 90.38% IoU for the lodging class on the combined dataset. Under sequential regional adaptation, performance remains relatively stable in continuous parcels, and degradation is less severe than most compact baselines in highly fragmented landscapes. On Jetson Nano, CASGNet achieves 1.94 FPS embedded inference under the 5 W mode. Smaller networks achieve higher speed but show reduced structural continuity in complex scenes. The results indicate that CASGNet provides a favorable balance between structural fidelity and computational cost, while its robustness remains constrained by scene complexity. Full article

Unmanned aerial vehicles (UAVs) provide a flexible platform for surface reflectance measurement at spatial scales between ground observations and satellite remote sensing. This study develops a UAV-based spectroradiometric system for surface reflectance retrieval under natural illumination conditions using non-imaging hyperspectral sensors. The system integrates two stabilized spectroradiometers mounted on a UAV to simultaneously measure hemispherical downwelling irradiance and upwelling surface radiance at flight altitude, enabling reflectance retrieval through a radiance–irradiance ratio framework without relying on ground calibration targets or radiative transfer model inversion. Field experiments were conducted over agricultural plots, and the UAV-derived reflectance was quantitatively validated against ground-based dual-spectroradiometer measurements. The results demonstrate stable irradiance measurements during flight and good agreement between UAV- and ground-derived reflectance across the 400–900 nm spectral range. The proposed system offers a practical and reliable solution for hyperspectral reflectance retrieval using UAV platforms. Full article