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نتیجه جستجو - Weeding

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Collection weeding: Innovative processes and tools to ease the burden
جمع آوری علفهای هرز : فرایندها و ابزارهای نوآورانه برای کاهش بار-2020
Evaluating collections and ultimately removing content poses a variety of difficult issues, including choosing appropriate deselection criteria, communicating with stakeholders, providing accountability, and managing the overall timetable to finish projects on time. The Science and Engineering librarians at Brigham Young University evaluated their entire print collection of over 350,000 items within one year, significantly reducing the number of items kept on the open shelves and the physical collection footprint. Keys to accomplishing this project were extensive preparation, tracking progress and accountability facilitated by Google Sheets and an interactive GIS stacks map, and stakeholder feedback facilitated by a novel web-based tool. This case study discusses guidelines to follow and pitfalls to avoid for any organization that is considering a large- or small-scale collection evaluation project.
Keywords: Weeding | Academic libraries | Collection management | Deselection of library materials | Collection evaluation
مقاله انگلیسی
2 Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland
مقیاس کنترل الگوریتم های بینایی ماشین برای تشخیص Rumex و Urtica در چمنزار-2017
Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision meth- ods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standard- ised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machine.1© 2017 Elsevier B.V. All rights reserved.
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