基于深度学习的支持向量机和卷积神经网络算法预测茄子病害

Venkataramana Attada, K. Kumar, N. Suganthi, R. Rajeswari
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引用次数: 4

摘要

植物病原体预测是在复杂环境中及时有效控制植物病原体的前提。然而,白霉病是茄子植物中的一种复杂病害。因此,为了克服这些困难,提出了一种新的深度学习集成(DLI)技术。该系统采用支持向量机(SVM)进行分类,卷积神经网络(CNN)算法进行预测,对茄子病害进行预测,准确率高达99.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Brinjal Plant Disease Using Support Vector Machine and Convolutional Neural Network Algorithm Based on Deep Learning
Plant pathogens prediction is the prerequisite for timely and productive control of plant pathogens within complicated environments. However, the white mold is a complicated disease in a brinjal plant. Hence, to vanquish these difficulties a novel Deep Learning Integration (DLI) Techniques has been proposed. In Proposed system, classification is carried out by Support Vector Machine (SVM) and prediction is carried out by Convolutional Neural Network (CNN) Algorithm to predict the plant illness in Brinjal with high accuracy of 99.4%.
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