Implementation of Bot Telegram as Broadcasting Media Classification Results of Convolutional Neural Network (CNN) Images of Rice Plant Leaves

Adi Fajaryanto; Fauzan Masykur; Mohammad  Rizqi Rosyadi

doi:10.47709/cnahpc.v5i1.1976

Authors

Adi Fajaryanto Universitas Muhammadiyah Ponorogo
Fauzan Masykur Universitas Muhammadiyah Ponorogo
Mohammad Rizqi Rosyadi Universitas Muhammadiyah Ponorogo

DOI:

10.47709/cnahpc.v5i1.1976

Keywords:

CNN, Quality of Service, Rice Plants, Deep Learning

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Abstract

Rice plants play an important role in the life of the Indonesian people because rice is the raw material for rice as a staple food. The rice production process does not rule out the possibility of interference by pests and diseases resulting in losses that cause crop failure. Meanwhile, pests on rice plants can be caused by various types, namely types of fungi (leafblast, hispa, brownspot) and types of nuisance animals. In this research, it will be carried out how to classify the image of rice plant leaves using the deep learning Convolutional Neural Network (CNN) algorithm, then the results of the classification are sent to users by utilizing the telegram chat application. The rice plant leaf image dataset is grouped into 4 groups (leafblast, brownspot, hispa and healthy). From several experiments it can be seen the results of system performance, namely the classification speed takes 30-60 seconds.

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