CD Skripsi

Optimasi Algoritma Convolutional Neural Network Untuk Deteksi Penyakit Padi Berbasis Arsitektur Mobilenetv3-Large Termodifikasi

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Rice is a crop that produces rice and is one of the largest commodities in the agricultural sector. Rice is the staple food for the Indonesian people, so stable production is needed to meet domestic demand. However, data shows a decline in production caused by diseases affecting rice plants. The lack of knowledge among farmers in detecting and identifying diseases is a significant factor contributing to the decreased productivity of rice. The use of artificial intelligence with the MobileNetV3-Large model can classify diseases in rice plants. However, the accuracy of this model is relatively low. This research aims to optimize the MobileNetV3-Large model by modifying its architecture to achieve higher accuracy. The types of diseases studied in this research are leaf blast, neck blast, leaf scald, and healthy leaves. The outcome of this research is a model that can detect rice diseases through imagery. The modified model achieved an accuracy of 93% with quite good performance. This model has higher accuracy and performance compared to the base model.
Keywords: Convolutional Neural Network, MobileNetV3-Large, Image Classification, Rice Plant Diseases.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2003113248

Penerbit

Pekanbaru : Universitas Riau FMIPA Sistem Informasi., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2003113248

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

SISTEM INFORMASI

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

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