Identifying Beetle Species Using Machine Learning
| dc.citation.ctitle | 1st Plant Pathology REEU Poster Symposium, Summer 2019 | |
| dc.contributor.author | Whitten, Samantha | |
| dc.date.accessioned | 2019-08-23T19:36:22Z | |
| dc.date.available | 2019-08-23T19:36:22Z | |
| dc.date.issued | 2019 | |
| dc.date.published | 2019 | |
| dc.description.abstract | Machine learning Artificial Intelligence (AI) hold the potential to benefit farmers and the environment. Computer models can identify lady beetles in images, and, with more training, possibly determine their presence in crop fields. As predators, lady beetles could be a strong indicator of aphid infestations. Using this information and Al technology, farmers could simultaneously reduce costs and environmental damage by having the ability to identify an infested area and focus pesticide applications on a specified section rather than on an entire field,. Before we reach this point, we must determine whether Al or human identification is more reliable and efficient. | |
| dc.description.conference | 1st Plant Pathology REEU Poster Symposium, Summer 2019 | |
| dc.description.sponsorship | This work is supported by the NIFA Research and Extension Experiences for Undergraduates (REEU) Program grant no. 2019-67032-29071/project accession no. 1018045 from the U.S. Department of Agriculture, National Institute of Food and Agriculture. | |
| dc.identifier.uri | http://hdl.handle.net/2097/40107 | |
| dc.language.iso | en_US | |
| dc.rights | © Author(s). This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.subject | USDA NIFA 2019-67032-29071 | |
| dc.title | Identifying Beetle Species Using Machine Learning | |
| dc.type | Text |
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