基于残差网络50 (RESNET50)和支持向量机(SVM)建模的水稻病害分类

Douaa S. Alwan, M. Naji
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引用次数: 0

摘要

水稻是全球最重要的粮食作物之一。因此,农民必须通过人工智能和深度学习技术保护这种作物的生产免受病虫害的感染,从而导致其破坏。将残差网络50 (ResNet50)深度卷积神经网络(CNN)与支持向量机(SVM)相结合,建立了水稻病害诊断模型。农民或从事农业工作的人可以使用该模型快速准确地识别作物中的疾病并进行治疗,从而提高作物产量,减少对昂贵且耗时的人工检查的需求。利用有效的图像分类深度学习模型ResNet50对水稻植物图像进行特征提取。然后根据这些特征使用SVM对疾病进行分类。ResNet50能够捕获图像中的复杂模式,而SVM能够使用这些模式做出准确的分类决策。该杂交模型具有较高的水稻病害诊断精度,准确率约为99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling
The rice crop is one of the most important food crops that depend on it globally. Therefore, farmers must preserve the production of this crop from infection with pests and diseases that lead to its destruction through artificial intelligence and deep learning techniques. A hybrid model combining a Residual Network 50 (ResNet50) deep convolutional neural network (CNN) and a support vector machine (SVM) developed diagnoses rice diseases. Farmers or people working in agriculture could use this model to quickly and accurately identify the diseases in their crops and treat them, increasing crop yield and reducing the need for costly and time-consuming manual inspection. ResNet50, a deep learning model effective at image classification tasks, was used to extract features from images of rice plants. SVM was then used to classify the diseases based on these features. The ResNet50 was able to capture complex patterns in the images, while the SVM was able to use these patterns to make accurate classification decisions. This hybrid model allowed for high precision in rice disease diagnosis, achieving an accuracy of approximately 99%.
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