3 results match your criteria: "Institute of Remote Sensing and Digital Agriculture[Affiliation]"
Improving rapeseed carbon footprint evaluation via the integration of remote sensing technology into an LCA approach.
Sci Total Environ
October 2024
Chair of Management, Innovation and Sustainable Business, University of Augsburg, Augsburg, Germany. Electronic address:
Agricultural carbon footprint (CF) evaluation plays an important role in climate change mitigation and national food security. Many studies have been conducted worldwide to evaluate the CF of rapeseed and its byproducts; however, only a few of these studies have considered finer-scale spatial-temporal heterogeneity. Considering the advantages of using detailed crop information extracted by remote sensing (RS) techniques, we attempted to integrate RS into life cycle assessments to improve rapeseed CF evaluation.
View Article and Find Full Text PDFDetection of citrus diseases in complex backgrounds based on image-text multimodal fusion and knowledge assistance.
Front Plant Sci
November 2023
Institute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, China.
Article Synopsis
- Diseases are a major threat to the citrus industry, making early and accurate detection crucial for control and management.
- This study aimed to create a more robust citrus disease classification model by enhancing data diversity and utilizing both image and textual data for a comprehensive analysis.
- The resulting multimodal model, using ShuffleNetV2 for images and TextCNN for text, achieved an impressive 98.33% accuracy on mixed datasets, significantly outperforming traditional single-modal approaches.
Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR.
Sci Total Environ
December 2021
Dept. of Civil, Environmental and Architectural Engineering, University of Padova, Padova 35121, Italy; UNESCO-LaSII (Land Subsidence International Initiative), Querétaro, Mexico.
The demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies.
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