Machine learning techniques for tomato plant diseases clustering, prediction and classification

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dc.authoridRadwan Qasrawi / 0000-0001-8671-7026en_US
dc.authorscopusidRadwan Qasrawi / 57212263325
dc.authorwosidRadwan Qasrawi / AAA-6245-2019
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorAmro, Malak
dc.contributor.authorZaghal, Raid
dc.contributor.authorSawafteh, Mohammad
dc.contributor.authorVicuna Polo, Stephanny
dc.date.accessioned2022-04-18T14:30:02Z
dc.date.available2022-04-18T14:30:02Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe agriculture sector in Palestine faces several challenges that affect the quality of crop yields, including plant diseases. Plant diseases may be caused by bacteria, viruses, and fungus, among others. Early detection and classification of these diseases allow farmers to reduce the instances and control the effect that the disease may have on their crops. Therefore, this study utilizes machine learning models for the clustering, prediction, and classification of tomato plant diseases in Palestine. The study used 3000 smartphone digital images of five tomato plant diseases (Alternaria Solani; Botrytis Cinerea; Panonychus Citri; Phytophthora Infestans; Tuta Absoluta) collected from three districts across the West Bank (Tulkarem, Jenin, and Tubas). The machine learning models used image embedding and hierarchical clustering techniques in clustering, and the neural network, random Forest, naïve Bayes, SVM, Decision Tree, and Logistic regression for prediction and classification. The models’ accuracy was validated in reference to a tomato plant diseases database created by plant pathogens experts. The clustering model provided 7 diseases clustering with an accuracy rate of 70%, while the neural network and logistic regression models reported performance accuracies of 70.3% and 68.9% respectively. The findings demonstrate that the proposed approach provides acceptable accuracy rates in the clustering, detection, and classification of tomato plant disease. Thus, the deployment of machine learning techniques in the agriculture sector might help Palestinian farmers better manage and control tomato diseases.en_US
dc.identifier.citationQasrawi, R., Amro, M., Zaghal, R., Sawafteh, M., Vicuna Polo, S. (2021). Machine Learning Techniques for Tomato Plant Diseases Clustering, Prediction and Classification. 2021 International Conference on Promising Electronic Technologies (ICPET).en_US
dc.identifier.doi10.1109/ICPET53277.2021.00014en_US
dc.identifier.isbn978-1-6654-1662-7
dc.identifier.issn2767-7044en_US
dc.identifier.scopus2-s2.0-85126743437en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/0.1109/ICPET53277.2021.00014
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2643
dc.identifier.wosWOS:000783368200008en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorQasrawi, Radwan
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofInternational Conference on Promising Electronic Technologies (ICPET)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectNeural Networksen_US
dc.subjectCropsen_US
dc.subjectPredictive Modelsen_US
dc.subjectAgricultureen_US
dc.subjectViruses (medical)en_US
dc.subjectRegression Tree Analysisen_US
dc.titleMachine learning techniques for tomato plant diseases clustering, prediction and classificationen_US
dc.typeConference Objecten_US

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