基于深度神经网络模型的柑橘果叶病害检测研究综述

C. Vinothini, Aditi Anand Huralikoppi, Kunche Nithyasree Royal, Guduru Rama Koushika, Koppala Jyoshna
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引用次数: 0

摘要

在农业上,柑橘果实产量下降主要是由于柑橘果实和叶片发生病害引起的。通过设计卷积神经网络(CNN)模型的自动检测系统,可以使用深度学习策略进行疾病的早期检测。柑桔病的果实和叶子,如痂、黑斑、溃疡病、黑糖病和绿色病,通过多层的结合和融合,从健康的叶子中分离出来。主要目的是建立一个识别疾病的模型,并为图像分配相应的疾病类别。深度学习分类器使用具有最优参数集的多层分类器,而机器学习分类器使用经典的特征表示方法对图像进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Literature Review on Citrus Fruits and Leaves diseases detection using Deep Neural Network model
In Agriculture, decline in the yield of citrus fruits is mainly caused by diseases occurring in citrus fruits and leaves. Early detection of diseases involves usage of deep learning strategies by designing an automated detection system with a convolutional neural network (CNN) model. The diseased citrus fruits and leaves of Scab, Black Spot, Canker, Melanose, and Greening are separated from healthy leaves by combining and fusing several layers. The main purpose is to build a model which identifies the disease and allocates the corresponding disease class to the image. Deep learning classifiers uses many layers with optimal parameter set whereas classical feature representation methods are employed by Machine Learning classifiers to classify images.
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