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December 7, 2025
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January 13, 2026
Published
January 17, 2026
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Abstract

Tomato leaf disease can cause a decline in productivity and crop failure, making early detection very important in precision farming practices. Manual detection methods, which are still commonly used in the field, have limitations in terms of speed and accuracy, requiring an automated image-based approach. Convolutional Neural Networks (CNNs) have become a leading technique in plant disease classification, but the diversity of architecture used requires systematic study to identify the most effective model. This study summarizes, compares, and evaluates CNN models for tomato leaf disease detection through a Systematic Literature Review (SLR) that adopts the PRISMA guidelines, covering the stages of identification, screening, feasibility assessment, and inclusion. A search in Scopus (2022–2025) using the query: (“Convolutional Neural Network” OR ‘CNN’) AND (‘tomato’ AND “leaf disease detection”) yielded 21 relevant articles. Analysis shows common preprocessing such as image resizing, data augmentation, and denoising. The best CNN architecture is InceptionV3 (most frequently used and high performing), followed by DenseNet201, MobileNetV2, and ResNet152V2. Architectures with optimal depth and high computational efficiency are preferred. This study provides a comprehensive map of CNN models to support architecture selection in tomato leaf disease detection. Future research directions include improving image quality, integrating attention mechanisms, semantic segmentation, and developing concise and efficient models for field applications.

Keywords

Convolutional Neural Network (CNN) Plant Disease Detection Tomato Deep Learning Systematic Literature Review

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Copyright (c) 2026 Fiki Sanora, Naufal Hafizh Mufafaq, Shofwatul Uyun

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Sanora, F., Mufafaq, N. H. ., & Uyun, S. (2026). A Systematic Review of Convolutional Neural Network Models for Tomato Leaf Disease Detection. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 5(1), 54–61. https://doi.org/10.20885/snati.v5.i1.45303
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