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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
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 …
Applications Of Optimization Modeling In Multi-Disciplinary Engineering Research, Anton F. Astner, Ekramul Haque Ehite, Yang Li, Colin Sasthav
Applications Of Optimization Modeling In Multi-Disciplinary Engineering Research, Anton F. Astner, Ekramul Haque Ehite, Yang Li, Colin Sasthav
Biosystems Engineering and Soil Science Publications and Other Works
Optimization modeling is the process of selection of the best solution to a design problem using predetermined constraints from a set of prospective solutions. Increased computing power has made optimization solvers readily available for business/research needs. For example, Microsoft Excel has a simple, but robust solver. Such solvers can model linear, nonlinear, and integer programming problems that are limited in size. This study shows the use of the optimization model solvers in various research contexts.