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All Journal Jurnal Teknologi dan Manajemen Informatika JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Applied Information System and Management Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMA InfoSys JSiI (Jurnal Sistem Informasi) Journal Medical Informatics Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Indra, Muhamad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health

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Journal : Journal Medical Informatics Technology

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Optimisation of Image Morphology Operations with Enhancement and Convolution in Tomato Leaf Disease Symptom Recognition Indra, Muhamad
Journal Medical Informatics Technology Volume 3 No. 3, September 2025
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v3i3.50

Abstract

Tomato (Solanum lycopersicum) is an important horticultural crop that is highly susceptible to various leaf diseases such as leaf spot, bacterial wilt, and fruit rot, which significantly reduce yield and quality. This study applies digital image processing techniques including pre-processing, morphology, enhancement, and convolution to improve the recognition of disease symptoms on tomato leaves. Pre-processing using grayscale conversion and median blur effectively reduces noise and sharpens essential details, while morphological operations (erosion and dilation) highlight structural features of infected areas. Enhancement techniques increase image contrast, making the distinction between healthy and diseased tissue more visible. Convolution methods with kernels such as Sobel and Gabor further emphasize edges and texture patterns of leaf lesions. Experimental results show that these methods improve pixel intensity distribution and enhance the visibility of disease symptoms, thereby increasing diagnostic accuracy. The integration of these techniques demonstrates the potential for early detection and classification of tomato leaf diseases, enabling more effective disease management and prevention of crop losses.
Co-Authors Amestina Matualage Andi Saputra Arniawati, Arniawati Baisa, Lorna Yertas Dedi Iskandar Inan I. Inan, Dedi Inan, Dedi I Inan, Dedi I. Inan, Dedi Iskandar Iskandar Inan, Dedi Juita, Ratna` Kandami, Joshua Hans Moata, Omega Joel Patria Patabang, Mety Rahmawati, Nanes Fitri Ratna Juita Sanglise, Marlinda Sipahelut, Novelia Soviana, Rita Vebiyatama, Andicha Wake, Tresia Yembise, Roberth