Search Results (1,564)

Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection. Full article

The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs. Full article

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach. Full article

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring. Full article

As a major contributor to environmental pollution in recent years, solid waste has become an increasingly significant concern in the realm of sustainable development. Unmanned Aerial Vehicle (UAV) imagery, known for its high spatial resolution, has become a valuable data source for solid waste detection. However, manually interpreting solid waste in UAV images is inefficient, and object detection methods encounter serious challenges due to the patchy distribution, varied textures and colors, and fragmented edges of solid waste. In this study, we proposed an improved instance segmentation approach called Watershed Mask Network for Solid Waste (WMNet-SW) to accurately retrieve solid waste with precise edges from UAV images. This approach combined the well-established Mask R-CNN segmentation framework with the watershed transform edge detection algorithm. The benchmark Mask R-CNN was improved by optimizing the anchor size and Region of Interest (RoI) and integrating a new mask head of Layer Feature Aggregation (LFA) to initially detect solid waste. Subsequently, edges of the detected solid waste were precisely adjusted by overlaying the segments generated by the watershed transform algorithm. Experimental results show that WMNet-SW significantly enhances the performance of Mask R-CNN in solid waste retrieval, increasing the average precision from 36.91% to 58.10%, F1-score from 0.5 to 0.65, and AP from 63.04% to 64.42%. Furthermore, our method efficiently detects the details of solid waste edges, even overcoming the limitations of training Ground Truth (GT). This study provides a solution for retrieving solid waste with precise edges from UAV images, thereby contributing to the protection of the regional environment and ecosystem health. Full article

Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations impractical. Few-shot semantic segmentation, which enables effective generalization from limited labeled samples, thus becomes essential for coastal region analysis. In this work, we propose an optimized few-shot segmentation method based on the Segment Anything Model (SAM) with a frozen-parameter segmentation backbone to improve generalization. To address the high visual similarity among coastal vegetation classes, we design a cross-matching module integrated with a hyper-correlation pyramid to enhance fine-grained visual correspondence. Additionally, a self-matching module is introduced to mitigate scale variations caused by UAV altitude changes. Furthermore, we construct a novel few-shot segmentation dataset, OUC-UAV-SEG-$2^{\mathrm{i}}$, based on the OUC-UAV-SEG dataset, to alleviate data scarcity. In quantitative experiments, the suggested approach outperforms existing models in mIoU and FB-IoU under ResNet50/101 (e.g., ResNet50’s 1-shot/5-shot mIoU rises by 4.69% and 4.50% vs. SOTA), and an ablation study shows adding CMM, SMM, and SAM boosts Mean mIoU by 4.69% over the original HSNet, significantly improving few-shot semantic segmentation performance. Full article

Small unmanned aerial vehicles (UAVs) pose significant security challenges due to their low detectability in infrared imagery, particularly when appearing as small, low-contrast targets against complex backgrounds. This paper presents a novel infrared target detection framework that addresses these challenges through two key innovations: an improved Gaussian filtering-based image enhancement module and a hierarchical feature extraction network. The proposed image enhancement module incorporates a vertical weight function to handle abnormal feature values while preserving edge information, effectively improving image contrast and reducing noise. The detection network introduces the SODMamba backbone with Deep Feature Perception Modules (DFPMs) that leverage high-frequency components to enhance small target features. Extensive experiments on the custom SIDD dataset demonstrate that our method achieves superior detection performance across diverse backgrounds (urban, mountain, sea, and sky), with mAP@0.5 reaching 96.0%, 74.1%, 92.0%, and 98.7%, respectively. Notably, our model maintains a lightweight profile with only 6.2M parameters and enables real-time inference, which is crucial for practical deployment. Real-world validation experiments confirm the effectiveness and efficiency of the proposed approach for practical UAV detection applications. Full article

This paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2). The system feeds from multispectral images captured by Unmanned Aerial Vehicles (UAVs) alongside data from IoT nodes. The primary objective is to improve classification performance in terms of both accuracy and precision. This is achieved by integrating contemporary Deep Learning techniques, specifically different CNN models, a prevalent type of artificial neural network composed of multiple interconnected layers, tailored for the analysis of agricultural imagery. The initial layers are responsible for identifying basic visual features such as edges and contours, while deeper layers progressively extract more abstract and complex patterns, enabling the recognition of intricate shapes. In this study, different datasets of tropical crop images, in this case banana crops, were constructed to evaluate the performance and accuracy of CNNs in detecting diseases in the crops, supported by transfer learning. For this, multispectral images are used to create false-color images to discriminate disease through spectra related to the blue, green and red colors in addition to red edge and near-infrared. Moreover, we used IoT nodes to include environmental data related to the temperature and humidity of the environment and the soil. Machine Learning models were evaluated and fine-tuned using standard evaluation metrics. For classification, we used fundamental metrics such as accuracy, precision, and the confusion matrix; in this study was obtained a performance of up to 86.5% using current deep learning models and up to 98.5% accuracy using the proposed hybrid and hierarchical architecture (SVMobileNetV2). This represents a new paradigm to significantly improve classification using the proposed hybrid CNN-SVM architecture and UAV-based multispectral images. Full article

In Northeast Brazil, climatic factors and technology synergistically enhance melon productivity and fruit quality. However, the region requires further research on the efficient use of water resources, particularly in determining the crop coefficient (Kc), which comprises the evaporation coefficient (Ke) and the transpiration coefficient (Kcb). Air temperature affects crop growth and development, altering the spectral response and the Kcb. However, the direct influence of air temperature on Kcb and spectral response remains underemphasized. This study employed unmanned aerial vehicle (UAV) with RGB and Red-Green-NIR sensors imagery to extract biophysical parameters for improved water management in melon cultivation in semiarid northern Bahia. Field experiments were conducted during two distinct periods: warm (October–December 2019) and cool (June–August 2020). The ‘Gladial’ and ‘Cantaloupe’ cultivars exhibited higher Kcb values during the warm season (2.753–3.450 and 3.087–3.856, respectively) and lower during the cool season (0.815–0.993 and 1.118–1.317). NDVI-based estimates of Kcb showed strong correlations with field data (r > 0.80), confirming its predictive potential. The results demonstrate that UAV-derived NDVI enables reliable estimation of melon Kcb across seasons, supporting its application for evapotranspiration modeling and precision irrigation in the Brazilian semiarid context. Full article

Fast and precise monitoring of soil organic matter (SOM) during maize growth periods is crucial for real-time assessment of soil quality. However, the big challenge we usually face is that many agricultural soils are covered by crops or snow, and the bare soil period is short, which makes reliable SOM prediction complex and difficult. In this study, an unmanned aerial vehicle (UAV) was utilized to acquire multi-temporal hyperspectral images of maize across the key growth stages at the field scale. The auxiliary predictors, such as spectral indices (I), field management (F), plant characteristics (V), and soil properties (S), were also introduced. We used stepwise multiple linear regression, partial least squares regression (PLSR), random forest (RF) regression, and XGBoost regression models for SOM prediction, and the results show the following: (1) Multi-temporal remote sensing information combined with multi-source predictors and their combinations can accurately estimate SOM content across the key growth periods. The best-fitting model depended on the types of models and predictors selected. With the I + F + V + S predictor combination, the best SOM prediction was achieved by using the XGBoost model (R² = 0.72, RMSE = 0.27%, nRMSE = 0.16%) in the R3 stage. (2) The relative importance of soil properties, spectral indices, plant characteristics, and field management was 55.36%, 26.09%, 9.69%, and 8.86%, respectively, for the multiple periods combination. Here, this approach can overcome the impact of the crop cover condition by using multi-temporal UAV hyperspectral images combined with valuable auxiliary variables. This study can also improve the field-scale farmland soil properties assessment and mapping accuracy, which will aid in soil carbon sequestration and soil management. Full article

To address the challenges of low detection accuracy and weak generalization in UAV aerial imagery caused by complex ground environments, significant scale variations among targets, dense small objects, and background interference, this paper proposes an improved lightweight multi-scale small-object detection model, MBD-YOLO (MBFF module, BiMS-FPN, and Dual-Stream Head). Specifically, to enhance multi-scale feature extraction capabilities, we introduce the Multi-Branch Feature Fusion (MBFF) module, which dynamically adjusts receptive fields through parallel branches and adaptive depthwise convolutions, expanding the receptive field while preserving detail perception. We further design a lightweight Bidirectional Multi-Scale Feature Aggregation Pyramid Network (BiMS-FPN), integrating bidirectional propagation paths and a Multi-Scale Feature Aggregation (MSFA) module to mitigate feature spatial misalignment and improve small-target detection. Additionally, the Dual-Stream Head with NMS-free architecture leverages a task-aligned architecture and dynamic matching strategies to boost inference speed without compromising accuracy. Experiments on the VisDrone2019 dataset demonstrate that MBD-YOLO-n surpasses YOLOv8n by 6.3% in mAP50 and 8.2% in mAP50–95, with accuracy gains of 17.96–55.56% for several small-target categories, while increasing parameters by merely 3.1%. Moreover, MBD-YOLO-s achieves superior detection accuracy, efficiency, and generalization with only 12.1 million parameters, outperforming state-of-the-art models and proving suitable for resource-constrained embedded deployment scenarios. The superior performance of MBD-YOLO, which harmonizes high precision with low computational demand, fulfills the critical requirements for real-time deployment on resource-limited UAVs, showing great promise for applications in traffic monitoring, urban security, and agricultural surveying. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

Automated Road defect detection using Unmanned Aerial Vehicles (UAVs) has emerged as an efficient and safe solution for large-scale infrastructure inspection. However, object detection in aerial imagery poses unique challenges, including the prevalence of extremely small targets, complex backgrounds, and significant scale variations. Mainstream deep learning-based detection models often struggle with these issues, exhibiting limitations in detecting small cracks, high computational demands, and insufficient generalization ability for UAV perspectives. To address these challenges, this paper proposes a novel comprehensive network, RoadNet, specifically designed for high-precision road defect detection in UAV-captured imagery. RoadNet innovatively integrates Transformer modules with a convolutional neural network backbone and detection head. This design not only significantly enhances the global feature modeling capability crucial for understanding complex aerial contexts but also maintains the computational efficiency necessary for potential real-time applications. The model was trained and evaluated on a self-collected UAV road defect dataset (UAV-RDD). In comparative experiments, RoadNet achieved an outstanding mAP@0.5 score of 0.9128 while maintaining a fast-processing speed of 210.01 ms per image, outperforming other state-of-the-art models. The experimental results demonstrate that RoadNet possesses superior detection performance for road defects in complex aerial scenarios captured by drones. Full article

Unmanned aerial vehicle (UAV) imagery provides an efficient approach for monitoring cotton defoliation and boll-opening rates. Deep learning, particularly convolutional neural networks (CNNs), has been widely applied in image processing and agricultural monitoring, achieving strong performance in tasks such as disease detection, weed recognition, and yield prediction. However, existing models often suffer from heavy computational costs and slow inference speed, limiting their real-time deployment in agricultural fields. To address this challenge, we propose a lightweight cotton maturity recognition model, RTCMNet (Real-time Cotton Monitoring Network). By incorporating a multi-scale convolutional attention (MSCA) module and an efficient feature fusion strategy, RTCMNet achieves high accuracy with substantially reduced computational complexity. A UAV dataset was constructed using images collected in Xinjiang, and the proposed model was benchmarked against several state-of-the-art networks. Experimental results demonstrate that RTCMNet achieves 0.96 and 0.92 accuracy on defoliation rate and boll-opening rate classification tasks, respectively. Meanwhile, it contains only 0.35 M parameters—94% fewer than DenseNet121—and only requires an inference time of 33 ms, representing a 97% reduction compared to DenseNet121. Field tests further confirm its real-time performance and robustness on UAV platforms. Overall, RTCMNet provides an efficient and low-cost solution for UAV-based cotton maturity monitoring, supporting the advancement of precision agriculture. Full article

The responsibility for risk assessment and user safety in forested and recreational areas lies with the property owner. This study shows that unmanned aerial vehicles (UAVs), combined with remote sensing and GIS analysis, effectively support the identification of high-risk trees, particularly those with reduced structural stability. UAV-based surveys successfully detect 78% of dead or declining trees identified during ground inspections, while significantly reducing labor and enabling large-area assessments within a short timeframe. The study covered an area of 6.69 ha with 51 reference trees assessed on the ground. Although the multispectral camera also recorded the red-edge band, it was not included in the present analysis. Compared to traditional ground-based surveys, the UAV-based approach reduced fieldwork time by approx. 20–30% and labor costs by approx. 15–20%. Orthomosaics generated from images captured by commercial multispectral drones (e.g., DJI Mavic 3 Multispectral) provide essential information on tree condition, especially mortality indicators. UAV data collection is fast and relatively low-cost but requires equipment capable of capturing high-resolution imagery in specific spectral bands, particularly near-infrared (NIR). The findings suggest that UAV-based monitoring can enhance the efficiency of large-scale inspections. However, ground-based verification remains necessary in high-traffic areas where safety is critical. Integrating UAV technologies with GIS supports the development of risk management strategies aligned with the principles of precision forestry, enabling sustainable, more proactive and efficient monitoring of tree-related hazards. Full article