Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1472
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFernando, S.-
dc.contributor.authorNethmi, R.-
dc.contributor.authorSilva, A.-
dc.contributor.authorPerera, A.-
dc.contributor.authorDe Silva, R.-
dc.contributor.authorAbeygunawardhana, P.K.W.-
dc.date.accessioned2022-03-04T03:16:29Z-
dc.date.available2022-03-04T03:16:29Z-
dc.date.issued2020-12-10-
dc.identifier.uri978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1472-
dc.description.abstractGreenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attentiongrabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning.en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;-
dc.subjectGreenhousesen_US
dc.subjectDisease diagnosisen_US
dc.subjectImage processingen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Learningen_US
dc.subjectTomato Farmingen_US
dc.titleIntelligent Disease Detection System for Greenhouse with a Robotic Monitoring Systemen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357143en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020
Department of Computer Systems Engineering-Scopes

Files in This Item:
File Description SizeFormat 
Intelligent_Disease_Detection_System_for_Greenhouse_with_a_Robotic_Monitoring_System.pdf
  Until 2050-12-31
487.03 kBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.