dc.contributor.author | Islam, Sabrina | |
dc.contributor.author | Paul, Apurba | |
dc.contributor.author | Chowdhury, Oishe Roy | |
dc.contributor.author | Fiza, Fahmida Akhter | |
dc.date.accessioned | 2024-04-28T05:30:40Z | |
dc.date.available | 2024-04-28T05:30:40Z | |
dc.date.copyright | ©2023 | |
dc.date.issued | 2023-12 | |
dc.identifier.other | ID: 20121011 | |
dc.identifier.other | ID: 20121033 | |
dc.identifier.other | ID: 19121130 | |
dc.identifier.other | ID: 20121007 | |
dc.identifier.uri | http://hdl.handle.net/10361/22682 | |
dc.description | This final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2023. | en_US |
dc.description | Cataloged from PDF version of final year design project. | |
dc.description | Includes bibliographical references (pages 82-84). | |
dc.description.abstract | This work aims to design and implement a disease detection system for mango leaves. We
have proposed two design approaches for the disease detection system for mango leaves.
From these two design approaches, we have performed simulation and analysis to get the best
design approach. The best design approach includes a pest disease detection system with a
rover body. We have trained our model to detect 9 types of pest diseases in mango leaves
with the help of available datasets. We have performed the test of the pest disease detection
system with the rover body in a particular field. In that field, we have got 3 diseases that were
detected by our rover body system successfully. Our pest disease detection system can easily
expand up to the required height for the particular testing field and can detect successfully the
pest diseases of the mango leaves which completes our entire project. | en_US |
dc.description.statementofresponsibility | Sabrina Islam | |
dc.description.statementofresponsibility | Apurba Paul | |
dc.description.statementofresponsibility | Oishe Roy Chowdhury | |
dc.description.statementofresponsibility | Fahmida Akhter Fiza | |
dc.format.extent | 127 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Agricultural robotics | en_US |
dc.subject | Image processing | en_US |
dc.subject | Disease detection | en_US |
dc.subject | Mango leaves pest | en_US |
dc.subject.lcsh | Artificial intelligence--Agricultural applications. | |
dc.subject.lcsh | Plant diseases. | |
dc.title | Disease detection system of mango leaves | en_US |
dc.type | Project report | en_US |
dc.contributor.department | Department of Electrical and Electronic Engineering, Brac University | |
dc.description.degree | B. Electrical and Electronic Engineering | |