首页|Reports Summarize Machine Learning Research from University of Nebraska-Lincoln (Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation w ith Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughpu t ...)
Reports Summarize Machine Learning Research from University of Nebraska-Lincoln (Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation w ith Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughpu t ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news originating from Lincoln, Nebraska , by NewsRx editors, the research stated, “Advancements in imaging, computer vis ion, and automation have revolutionized various fields, including field-based hi gh-throughput plant phenotyping (FHTPP).” Financial supporters for this research include United States Department of Agric ulture. Our news correspondents obtained a quote from the research from University of Ne braska-Lincoln: “This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a po werful tool in FHTPP, particularly in crop segmentationidentifying crops from t he background-crucial for trait analysis. However, the effectiveness of DCNNs of ten hinges on the availability of large, labeled datasets, which poses a challen ge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB i mages, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Inter section-over-Union (IoU) than the threshold method and 11% over co nventional machine learning, with significantly faster prediction times and mana geable training duration.”
University of Nebraska-LincolnLincolnNebraskaUnited StatesNorth and Central AmericaCyborgsEmerging Technolog iesMachine Learning
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