dc.contributor.author |
Perera, Oshadha |
|
dc.date.accessioned |
2024-04-26T08:03:03Z |
|
dc.date.available |
2024-04-26T08:03:03Z |
|
dc.date.issued |
2023 |
|
dc.identifier.citation |
Perera, Oshadha (2023) Citrus fruit disease identification application. BSc. Dissertation, Informatics Institute of Technology |
en_US |
dc.identifier.issn |
20191139 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/2071 |
|
dc.description.abstract |
"The Citrus Fruit Disease Identification System is a cutting-edge system that seamlessly combines disease diagnosis and fertilizer suggestion features created exclusively for Citrus plants. This cutting-edge system gives growers the ability to identify diseases affecting their citrus crops precisely and reliably while also giving them personalized and tailored fertilizer recommendations to maximize plant health and productivity. It does this by leveraging the power of sophisticated machine learning algorithms. The cutting-edge technology uses sophisticated deep learning algorithms to extensively analyse visual representations of citrus fruit trees with astounding accuracy and quickly identify common ailments. Additionally, this modern system provides crucial insights into customized fertilizers
and treatment methods designed particularly to address the ailments discovered. Armed with this priceless information, growers can respond quickly and successfully to the problems at hand, assuring timely intervention and the deployment of appropriate treatments. " |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
Disease identification |
en_US |
dc.subject |
Image processing |
en_US |
dc.subject |
Android mobile application |
en_US |
dc.title |
Citrus fruit disease identification application |
en_US |
dc.type |
Thesis |
en_US |