Classification of Clove Leaf Blister Blight Disease Severity Using Pre-trained Model VGG16, InceptionV3, and ResNet

P. A. Pramesti, Muhamad Supriyadi, Muhammad Reza Alfin, Rita Noveriza, D. Wahyuno, D. Manohara, Melati, Miftakhurohmah, Riki Warman, S. Hardiyanti, Asnawi
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引用次数: 0

Abstract

Clove is one of the precious plants produced in Indonesia. Clove has many benefits for humans, but clove cultivation often experiences problems due to disease attacks, including Leaf Blister Blight Disease(CDC). The handling of CDC disease is carried out based on the severity of the symptoms that can be seen on the affected leaves. This research was conducted to obtain a CDC disease classification model, so appropriate treatment can be carried out. This study used the pre-trained VGG16, InceptionV3, and ResNet models for classification. VGG16 got the highest average accuracy of 96.7%. Aside from that, k-fold cross validation improved the model's accuracy.
使用预训练模型 VGG16、InceptionV3 和 ResNet 对丁香叶疱萎缩病严重程度进行分类
丁香是印度尼西亚出产的珍贵植物之一。丁香对人类有很多益处,但丁香种植经常会遇到病害侵袭的问题,其中包括叶疱枯萎病(CDC)。对 CDC 病害的处理是根据受害叶片上症状的严重程度进行的。本研究旨在获得 CDC 病害分类模型,以便进行适当的处理。本研究使用预先训练好的 VGG16、InceptionV3 和 ResNet 模型进行分类。VGG16 的平均准确率最高,达到 96.7%。此外,k-fold 交叉验证也提高了模型的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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