利用MobileNet、InceptionV3和CropNet对木薯植物病害进行分类

G. Oktavian, Handri Santoso
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引用次数: 0

摘要

在撒哈拉以南非洲,木薯被广泛种植,被认为是人类食物中碳水化合物的主要来源。然而,这种植物受到疾病的困扰,可能威胁到数百万人的食物供应。通过使用计算机视觉,研究人员试图创建一个图像分类模型,该模型可以通过拍摄叶子来告诉农民植物是否生病。在这篇短文中,作者尝试训练三个卷积神经网络:CropNet, MobileNet和InceptionV3,可以基于视觉数据对木薯植物病害进行分类。作为一种创新,作者创建了一个集成投票分类器,它结合了CropNet、MobileNet和InceptionV3的预测来创建更好的预测。事实证明,创建一个集成投票分类器使我们能够获得比每个模型的平均分数高6.8%的准确率分数。
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Leveraging MobileNet, InceptionV3, and CropNet to Classify Cassava Plant Disease
In sub-Saharan Africa, cassava is widely grown and considered to be a large source of carbohydrates for human food. However, the plant is plagued with diseases which can threaten food supply for millions of people. By using computer vision, researchers attempted to create an image classification model that can tell farmers whether the plant is sick or not by taking pictures of their leaves. In this short paper, the author attempts to train three Convolutional Neural Network: CropNet, MobileNet, and InceptionV3 that can classify cassava plant diseases based on visual data. As a novelty, the author creates an ensemble voting classifier that combines the prediction of CropNet, MobileNet, and InceptionV3 to create a better prediction. Turns out, creating an ensemble voting classifier enables us to achieve an accuracy score which is 6.8% higher than the average individual scores of each model.
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