EnConv: enhanced CNN for leaf disease classification

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dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 59417834100
dc.authorwosidFemilda Josephin Joseph Shobana Bai / JTQ-1812-2023
dc.contributor.authorThanjaivadivel, M.
dc.contributor.authorGobinath, C.
dc.contributor.authorVellingiri, J.
dc.contributor.authorKaliraj, S.
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.date.accessioned2025-04-17T14:06:09Z
dc.date.available2025-04-17T14:06:09Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractDetecting leaf diseases in plants is essential to maintain crop yield and market value. Machine learning has shown promise in detecting these diseases as it can group data into predetermined categories after examining it from various angles. However, machine learning models require a thorough knowledge of plant diseases, and processing time can be lengthy. This study proposes an enhanced convolutional neural network that utilizes depthwise separable convolution and inverted residual blocks to detect leaf diseases in plants. The model considers the morphological properties and characteristics of the plant leaves, including color, intensity, and size, to categorize the data. The proposed model outperforms traditional machine learning approaches and deep learning models, achieving an accuracy of 99.87% for 39 classes of different plants such as tomato, corn, apple, potato, and more. To further improve the model, global average pooling was used in place of the flatten layer. Overall, this study presents a promising approach to detect leaf diseases in plants using an enhanced convolutional neural network with depthwise separable convolution and inverted residual blocks. The results show the potential benefits of using this model in agriculture to improve the early detection of plant diseases and maintain crop yield and market value.
dc.description.sponsorshipManipal Academy of Higher Education
dc.identifier.citationThanjaivadivel, M., Gobinath, C., Vellingiri, J., Kaliraj, S., & Femilda Josephin, J. S. (2025). EnConv: enhanced CNN for leaf disease classification. Journal of Plant Diseases and Protection, 132(1), 1-12.
dc.identifier.doi10.1007/s41348-024-01033-6
dc.identifier.endpage12
dc.identifier.issn1861-3829
dc.identifier.issn1861-3837
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85212072290
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1007/s41348-024-01033-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6285
dc.identifier.volume132
dc.identifier.wosWOS:001378899000002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBai, Femilda Josephin Joseph Shobana
dc.institutionauthoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506
dc.language.isoen
dc.publisherSpringer science and business media deutschland GmbH
dc.relation.ispartofJournal of plant diseases and protection
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAugmentation
dc.subjectDepthwise
dc.subjectDepthwise Separable Convolutional Neural Network (DS-CNN)
dc.subjectDisease
dc.subjectECNN
dc.subjectLeaf
dc.subjectMax-Pool
dc.subjectSoft-Max
dc.titleEnConv: enhanced CNN for leaf disease classification
dc.typeArticle

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