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All Journal Jurnal Informatika Ekonomi Bisnis Jurnal Informatika Ekonomi Bisnis
Afwan Anggara
Program Studi Informatika, Universitas Teknologi Yogyakarta
Economics, Econometrics & Finance

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Implementation for Plant Disease Classification via Telegram Restu Gilang Prasetyo; Afwan Anggara
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026): Accepted
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1407

Abstract

This study aims to develop an automated system for classifying vegetable plant diseases using the MobileNet algorithm integrated with a Telegram Bot. The system is designed to assist users, especially farmers, in identifying plant diseases quickly and efficiently through leaf images. The research method applies a Convolutional Neural Network with the MobileNet architecture due to its lightweight and efficient computational performance. The dataset used in this study consists of tomato leaf images obtained from a public dataset on Kaggle, which includes several disease categories and healthy leaves. The system is implemented using Python and integrated with the Telegram Bot API to enable real-time interaction. The process begins when users upload leaf images, followed by image preprocessing and classification using the trained model. The results show that the system is capable of providing accurate classification with good performance and can handle various input conditions. In addition, the integration with Telegram makes the system easily accessible without requiring additional applications. Therefore, this study offers a practical and efficient solution for early detection of plant diseases using deep learning technology.
Implementation for Plant Disease Classification via Telegram Restu Gilang Prasetyo; Afwan Anggara
Jurnal Informatika Ekonomi Bisnis Vol. 8, No. 2 (June 2026): Accepted
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v8i2.1407

Abstract

This study aims to develop an automated system for classifying vegetable plant diseases using the MobileNet algorithm integrated with a Telegram Bot. The system is designed to assist users, especially farmers, in identifying plant diseases quickly and efficiently through leaf images. The research method applies a Convolutional Neural Network with the MobileNet architecture due to its lightweight and efficient computational performance. The dataset used in this study consists of tomato leaf images obtained from a public dataset on Kaggle, which includes several disease categories and healthy leaves. The system is implemented using Python and integrated with the Telegram Bot API to enable real-time interaction. The process begins when users upload leaf images, followed by image preprocessing and classification using the trained model. The results show that the system is capable of providing accurate classification with good performance and can handle various input conditions. In addition, the integration with Telegram makes the system easily accessible without requiring additional applications. Therefore, this study offers a practical and efficient solution for early detection of plant diseases using deep learning technology.
Co-Authors Restu Gilang Prasetyo