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All Journal Jurnal Teknik Informatika (JUTIF)
Andrianto, Diky
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Computer Science & IT

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COMPARISON OF MOBILENET AND CNN METHODS FOR IDENTIFYING TOMATO LEAF DISEASES Andrianto, Diky; Prathivi, Rastri; Liu, Meifang
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3477

Abstract

Tomato plants are usually easily attacked by diseases, either viruses or fungi, resulting in a significant reduction in the quality and quantity of crop production. Tomato production is at risk from various diseases affecting the leaves. Early diagnosis of these diseases allows farmers to take preventive action and protect their crops. The use of artificial intelligence, especially deep learning, has greatly improved plant disease detection systems. Advances in computer vision, particularly Convolutional Neural Networks (CNN), have shown reliable results in image classification and identification. Below is previous research on identifying tomato leaf diseases.
Co-Authors Liu, Meifang Rastri Prathivi