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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Ikhsanda, Reza
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Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Model EfficientNetV2L Dalam Mendeteksi Citra Penyakit Daun Tomat untuk Meningkatkan Hasil Panen Petani Mustopa, Ali; Khultsum, Umi; Sabaruddin, Raja; Ikhsanda, Reza; Firmansyah, Herman
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5886

Abstract

In the era of modern agriculture, farmers face increasingly complex challenges related to controlling tomato plant diseases. Lack of knowledge and in-depth understanding of the types of diseases that may occur in tomato leaves can result in errors in identifying plant health problems, which can ultimately disrupt productivity and sustainability of crop yields. There is a major urgency that drives this research is the need for a better understanding of the diseases that affect tomato plants. In addition, the need to develop accurate models to detect diseases quickly and efficiently and the importance of implementing solutions that are practical and easily accessible to farmers. This study aims to provide farmers with useful tools to recognize and treat tomato leaf diseases more effectively so that they can increase yields and significantly increase their income. The model developed is expected to be able to identify and classify various types of tomato leaf diseases with high accuracy. This study utilizes a deep learning method using a Convolutional Neural Network (CNN) based on the EfficientNetV2L architecture in the tomato leaf disease classification process. This study produces an accuracy of 97.22% in the classification process using the EfficientNetV2L architecture and the implementation of a model that can be easily adopted by farmers. The developed model is integrated into a web-based system that can be accessed by farmers widely.
Co-Authors Ali Mustopa, Ali Firmansyah, Herman Khultsum, Umi Raja Sabaruddin