Cowpea sorter: An alternative to the manual cowpea sorting process
| dc.contributor.author | Salley, Kawusara Nurudeen | |
| dc.date.accessioned | 2020-05-11T11:54:03Z | |
| dc.date.available | 2020-05-11T11:54:03Z | |
| dc.date.issued | 2019 | |
| dc.description | Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, April 2019 | en_US |
| dc.description.abstract | Cowpea traders find the manual sorting process of cowpeas laborious and time-consuming. Based on the volume of cowpeas and the proportion of damaged cowpeas, the process can span a time interval of about 6 hours. This project integrates the power of computer vision and convolutional neural networks to develop a solution for the cowpea trader to effectively segregate good cowpeas from the damaged ones. | en_US |
| dc.description.sponsorship | Ashesi University | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11988/543 | |
| dc.language.iso | en_US | en_US |
| dc.subject | agricultural products | en_US |
| dc.subject | cowpea retailer | en_US |
| dc.subject | Vigna unguiculata | en_US |
| dc.subject | quality evaluation | en_US |
| dc.subject | computer vision | en_US |
| dc.subject | Convolutional Neural Network (CNN) | en_US |
| dc.title | Cowpea sorter: An alternative to the manual cowpea sorting process | en_US |
| dc.type | Capstone project | en_US |