Leaf Disease Detection using Deep Learning

R. Anitha, A. Bazila Banu
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引用次数: 1

Abstract

Agriculture plays an important role in determining India's economy. So, the detection of disease that affects the plants is most important as it affects productivity. The proposed system is designed to detect the diseases that degrade the health of the leaves. The diseases may be of bacterial, viral and late blight. The diseases can be detected with the help of Convolutional Neural Network (CNN). It is composed of several layers that help in the prediction of diseases. The designed CNN classifies the disease into three major categories. An input leaf image is provided to test whether the leaf is healthy or not. The system has been trained with different input leaves. Once it is trained the new input leaves are given to the classifier, then the classifier identifies the label of the affected leaves. Based on the disease identified, the necessary remedies can be taken for curing the disease.
基于深度学习的叶片病害检测
农业在决定印度经济方面起着重要作用。因此,检测影响植物的疾病是最重要的,因为它会影响生产力。所提出的系统旨在检测降低叶子健康的疾病。这些疾病可能是细菌性、病毒性和晚疫病。这些疾病可以通过卷积神经网络(CNN)来检测。它由几层组成,有助于预测疾病。经过设计的CNN将这种疾病分为三大类。提供输入叶片图像以测试叶片是否健康。系统已经用不同的输入叶进行了训练。一旦它被训练,新的输入叶子就被给分类器,然后分类器识别受影响叶子的标签。根据确定的疾病,可以采取必要的补救措施来治疗疾病。
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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