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| http://dx.doi.org/10.1164/rccm.202207-1413LE | DOI Listing |
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In much of the northern Great Basin of the western United States, rangelands, and semi-arid ecosystems invaded by exotic annual grasses such as cheatgrass () and medusahead () are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground-based field methods for measuring the above-ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes and better harmonize with other remote sensing techniques. Developments and increased adoption of unoccupied aerial systems (UAS) and instrumentation for vegetation monitoring enable greater understanding of vegetation in many ecosystems.
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Despite the evident benefits of spectral computed tomography (CT) in delivering qualitative imaging superior to that of conventional CT in adults, its application in pediatric diagnostic imaging is still relatively limited due to various reasons, including design limitations and radiation dose considerations. The use of specialized K-edge filters, in conjunction with other spectral technologies, has been demonstrated to improve spectral quantification accuracy. X-ray flux limitations generally pose challenges in these concepts when applied to adults.
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Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery.
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Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets.
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