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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
The Business Case For Industrial Safety: Revealing The Comprehensive Value Of Ergonomic Investments For Manufacturing Enterprises In Industry 4.0, Shane Stan
Honors Theses
How can today’s manufacturing enterprises construct, implement, and optimize modern safety initiatives in a manner that will present maximum return on investment and facilitate enterprise growth? Furthermore, how can these manufacturers assure individual ergonomic investments become part of a larger strategy to facilitate organizational change in safety? This work addresses these questions by placing industrial ergonomics in a business improvement context which comprehensively presents the financial returns and growth opportunities poised by modern safety initiatives. Additionally, to further strengthen the business case for industrial safety, an ergonomic action planning framework is established to guide the creation of holistic safety programs …
Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar
Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
Mid-late season weeds are those that escape the early season herbicide applications and those that emerge late in the season. They might not affect the crop yield, but if uncontrolled, will produce a large number of seeds causing problems in the subsequent years. In this study, high-resolution aerial imagery of mid-season weeds in soybean fields was captured using an unmanned aerial vehicle (UAV) and the performance of two different automated weed detection approaches – patch-based classification and object detection was studied for site-specific weed management. For the patch-based classification approach, several conventional machine learning models on Haralick texture features were …