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Machine Learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features
Department of Computer Science and Engineering, Premier University, Chattogram, Bangladesh.
Department of Computer Science and Engineering, Premier University, Chattogram, Bangladesh.
Department of Computer Science and Engineering, Premier University, Chattogram, Bangladesh.
Department of Computer Science and Engineering, Premier University, Chattogram, Bangladesh.
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2023 (English)In: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022 / [ed] M. Shamim Kaiser; Sajjad Waheed; Anirban Bandyopadhyay; Mufti Mahmud; Kanad Ray, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 1, p. 25-35Conference paper, Published paper (Refereed)
Abstract [en]

Tomato leaves can be infected with various infectious viruses and fungal diseases that drastically reduce tomato production and incur a great economic loss. Therefore, tomato leaf disease detection and identification are crucial for maintaining the global demand for tomatoes for a large population. This paper proposes a machine learning-based technique to identify diseases on tomato leaves and classify them into three diseases (Septoria, Yellow Curl Leaf, and Late Blight) and one healthy class. The proposed method extracts radiomics-based features from tomato leaf images and identifies the disease with a gradient boosting classifier. The dataset used in this study consists of 4000 tomato leaf disease images collected from the Plant Village dataset. The experimental results demonstrate the effectiveness and applicability of our proposed method for tomato leaf disease detection and classification.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. Vol. 1, p. 25-35
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 618
Keywords [en]
Classification, Machine learning, Radiomics features, Tomato leaf disease
National Category
Computer Sciences Horticulture
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-99530DOI: 10.1007/978-981-19-9483-8_3Scopus ID: 2-s2.0-85163307111ISBN: 978-981-19-9482-1 (print)ISBN: 978-981-19-9483-8 (electronic)OAI: oai:DiVA.org:ltu-99530DiVA, id: diva2:1787235
Conference
4th International Conference on Trends in Computational and Cognitive Engineering, TCCE 2022, Tangail, Bangladesh, December 17-18, 2022
Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-08-11Bibliographically approved

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Andersson, Karl

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