Ear recognition and detection is a commonly used method in the yield prediction process for rice and wheat crops. In current agricultural technology research, rice and wheat ear are typically treated and modeled separ...
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Ear recognition and detection is a commonly used method in the yield prediction process for rice and wheat crops. In current agricultural technology research, rice and wheat ear are typically treated and modeled separately to improve detection accuracy. Due to the significant similarities in the phenotyping structures and physicochemical indicators of rice and wheat ear, a unified detection model can be developed to fulfill the requisite modeling specifications. Therefore, in response to the lack of research on the unified real-time detection of rice and wheat ear, this paper proposes a lightweight detection model, Light-Y, suitable for complex environments. The model utilizes the lightweight MobileNetV3 network combined with the dynamic detection head DyHead to reconstruct the YOLOv5s network. Through multi-scale feature aggregation and attention mechanisms, the model effectively enhances its ability to capture dense targets in complex scenarios while reducing computational redundancy. On this basis, rice and wheat ear data collected by smartphones and drones are used, along with transfer learning and a staged data introduction strategy, to achieve efficient integration of multi-sourcedata, significantly improving the generalization ability and adaptability of the model for detecting rice and wheat ear targets. Finally, channel pruning is applied to remove inefficient channels, effectively reducing computational costs and optimizing resource allocation efficiency. The experimental results show that the [email protected] of Light-Y reaches 91.9 %, an improvement of 0.4 %, with a weight of 4.68 MB, a parameter count of 2.2 × 10⁶, and FLOPs of 4 × 10⁹. In terms of accuracy, efficiency, and resource consumption, Light-Y outperforms existing mainstream models (e.g., YOLOv8n, YOLO11n). Further validation demonstrates that Light-Y achieves detection accuracy R² of 0.96, 0.95, 0.95, and 0.94 on the smartphone-based wheat ear dataset, smartphone-based rice ear dataset, dro
Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable...
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Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel;(2) lithological mapping based on spectral information;and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and SOndre StrOmfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for SOndre StrOmfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches.
Based on metadata technique, this paper introduced metadata service oriented multi-source heterogeneous information dataintegration frame for large multi-source heterogeneous information integration management and in...
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ISBN:
(纸本)9783038350125
Based on metadata technique, this paper introduced metadata service oriented multi-source heterogeneous information dataintegration frame for large multi-source heterogeneous information integration management and interconnecting, intercommunicating, interoperability between different heterogeneous information systems. This metadata service oriented frame, benefiting from knowledge base of data warehouse, had 5 layers frame system including resource layer, integration layer, management layer, service layer and customer layer. Metadata service system contained data layer, directory service logic layer, data exchange layer and Web presentation layer. This paper represented that the metadata service was the core of integration frame, and the realization of integration management platform was feasible.
The geometry of the bedrock, internal layers and slip surfaces control the deformation pattern and the mechanisms of landslides. A challenge to progress in understanding landslide behavior is to construct accurate 3D ...
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The geometry of the bedrock, internal layers and slip surfaces control the deformation pattern and the mechanisms of landslides. A challenge to progress in understanding landslide behavior is to construct accurate 3D geometrical models from different surveying techniques. The objective of this work is to present a methodology for integrating multi-source and multi-resolution data in a 3D geometrical model using geostatistical tools. The methodology consists in integrating the data by extracting relevant information on the internal structure of the landslide and in detecting possible incoherencies between different interpretations. A simple method to classify the input data and to control their influence on the model interpolation is proposed through the development of an expert reliability index. The methodology is applied for the creation of a 3D geometrical model of the Super-Sauze mudslide (South French Alps) for which an extensive dataset is available. Error calculation and expert geomorphological interpretation allow one to select the most suitable interpolation algorithm and to define the volumes of each layer. The proposed 3D geometrical model highlights the influence of the bedrock geometry on the observed kinematic pattern. (C) 2011 Elsevier B.V. All rights reserved.
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