Expert system for smart farming for diagnosis of sugarcane diseases using machine learning

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dc.contributor.authorAtheeswaran, Athirajacs
dc.contributor.authorK. V., Raghavendercs
dc.contributor.authorChaganti, B. N. Lakshmics
dc.contributor.authorMaram, Ashokcs
dc.contributor.authorHerencsár, Norbertcs
dc.coverage.issueJuly 2023cs
dc.coverage.volume109,Part Acs
dc.date.issued2023-07-03cs
dc.description.abstractAgriculture is one of the oldest occupations in the world and continues to exist today. In some form or another, the world's population depends on agriculture for its needs. The major loss in sugarcane production in India is due to pests, plant disease, malnutrition, and nutrient deficiency in plants. To identify these diseases, farmers go to local farmers, experts, agricultural people, and fellow neighbors to identify the problem caused. In some cases, their information may be adequate, but in others it is not. These people cannot solve all the problems caused by their crops can be solved by these people; there is a need to accurately predict the correct disease and provide the proper treatment at the right time. This can only be done by applying machine learning-based Internet of Things solutions in real time. This article proposes a method for a smart farming system to address the needs of farmers producing sugarcane in India by applying intelligent solutions that use image processing and soft computing. Four sugarcane diseases are investigated, such as Eyespot, Leaf Scald, Yellow Leaf, and Pokkah Boeng, and three characteristics such as color, shape, and texture. Images were used for training data in Artificial Neural Network (ANN), Neuro-Fuzzy, and Case-Based Reasoning (CBR) algorithms, and the performance of the feature extraction technique was evaluated in terms of sensitivity, specificity, F1 score, and accuracy.en
dc.description.embargo2025-05-16cs
dc.formattextcs
dc.format.extent1-14cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationCOMPUTERS & ELECTRICAL ENGINEERING. 2023, vol. 109,Part A, issue July 2023, p. 1-14.en
dc.identifier.doi10.1016/j.compeleceng.2023.108739cs
dc.identifier.issn0045-7906cs
dc.identifier.orcid0000-0002-9504-2275cs
dc.identifier.other183454cs
dc.identifier.researcheridA-6539-2009cs
dc.identifier.scopus23012051100cs
dc.identifier.urihttp://hdl.handle.net/11012/209424
dc.language.isoencs
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDcs
dc.relation.ispartofCOMPUTERS & ELECTRICAL ENGINEERINGcs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0045790623001635cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessembargoedAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0045-7906/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectANNen
dc.subjectCBRen
dc.subjectFeature extractionen
dc.subjectFuzzy logicen
dc.subjectMedian filteringen
dc.subjectNeuro-fuzzyen
dc.subjectSmart farmingen
dc.titleExpert system for smart farming for diagnosis of sugarcane diseases using machine learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-183454en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:30en
sync.item.modts2025.01.17 18:36:11en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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