| dc.contributor.author | Kalideen, Jawith | |
| dc.date.accessioned | 2023-01-20T05:43:46Z | |
| dc.date.available | 2023-01-20T05:43:46Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Kalideen, Jawith (2022) Plant Leaf Disease Prediction. BSc. Dissertation, Informatics Institute of Technology | en_US |
| dc.identifier.issn | 2018229 | |
| dc.identifier.uri | http://dlib.iit.ac.lk/xmlui/handle/123456789/1493 | |
| dc.description.abstract | "The growers and agricultural experts who labor so hard to develop and create a hunger-free community are the focus of this research endeavor. They don't get the respect they deserve for what they genuinely need. This effort will assist them in reducing stress and healing a few of their frequent ailments. To meet our project 's aims, we use a bunch of data science elements. Where any personal contacts are eliminated, machine learning is commonly used. Several difficult realworld issues can be simply handled with machine learning, even if we don't expressly program. Evaluating the vital information between the ones that have previously been saved and the ones that have been submitted for investigation is also a lengthy operation. This research may serve as a springboard for developing apps to address farming concerns in today's fast-paced, technologically advanced society, where classical and environmentally beneficial concepts coexist. This achievement shall undoubtedly clear the path for a healthy farming society. " | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Plant Leaf Disease | en_US |
| dc.subject | Prediction Using Machine Learning | en_US |
| dc.title | Plant Leaf Disease Prediction | en_US |
| dc.type | Thesis | en_US |