Artificial intelligence powered personalized agriculture
| dc.contributor.author | Tetala, Satya Surya Dattatreya Reddy, author | |
| dc.contributor.author | Simske, Steven, advisor | |
| dc.contributor.author | Conrad, Steve, committee member | |
| dc.contributor.author | Gaines, Todd, committee member | |
| dc.contributor.author | Nalam, Vamsi, committee member | |
| dc.date.accessioned | 2023-06-01T23:55:49Z | |
| dc.date.available | 2023-06-01T23:55:49Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The integration of Artificial Intelligence (AI) in agriculture has shown the potential to improve crop selection and enhance sustainability practices. In this study, we aim to investigate the benefits and feasibility of using AI-powered personalized recommendations for crop selection and sustainability practices in the context of agroecology. We propose to lay the foundation for an agricultural recommendation engine that considers several parameters that influence yield and presents the best crop(s) to sow based on the model's output. We aim to examine this recommendation engine's impact on agriculture's sustainability and to evaluate its effectiveness and accuracy. Our ultimate goal is to provide a comprehensive understanding of the potential benefits and challenges of using AI-powered recommendations in agriculture and to lay the foundation for the development of a practical, effective, and user-friendly recommendation engine that can help farmers make informed decisions about their crops and improve the long-term sustainability of agriculture. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Tetala_colostate_0053A_17622.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/236651 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | crop yield prediction | |
| dc.subject | recommendation systems | |
| dc.subject | crop profitability | |
| dc.subject | sustainable farming | |
| dc.subject | personalized agriculture | |
| dc.title | Artificial intelligence powered personalized agriculture | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). 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). | |
| thesis.degree.discipline | Systems Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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