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Full-Text Articles in Engineering
Electrochemical Biosensing And Deep Learning-Based Approaches In The Diagnosis Of Covid-19: A Review, Omer Sadak, Ferhat Sadak, Ozal Yildirim, Nicole M. Iverson, Rizwan Qureshi, Muhammed Talo, Chui Ping Ooi, U. Rajendra Acharya, Sundaram Gunasekaran, Tanvir Alam
Electrochemical Biosensing And Deep Learning-Based Approaches In The Diagnosis Of Covid-19: A Review, Omer Sadak, Ferhat Sadak, Ozal Yildirim, Nicole M. Iverson, Rizwan Qureshi, Muhammed Talo, Chui Ping Ooi, U. Rajendra Acharya, Sundaram Gunasekaran, Tanvir Alam
Biological Systems Engineering: Papers and Publications
COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The "gold standard'' for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns …
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 …
In Vivo Human-Like Robotic Phenotyping Of Leaf And Stem Traits In Maize And Sorghum In Greenhouse, Abbas Atefi
In Vivo Human-Like Robotic Phenotyping Of Leaf And Stem Traits In Maize And Sorghum In Greenhouse, Abbas Atefi
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
In plant phenotyping, the measurement of morphological, physiological and chemical traits of leaves and stems is needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, tedious, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, which enables automatic monitoring with minimal human intervention. In this study, two plant phenotyping robotic systems were developed to realize automated measurement of plant leaf properties and stem diameter which could reduce the tediousness of data collection compare to manual measurements. The robotic systems comprised of a four degree …