Weed-plant discrimination by machine vision and artificial neural network
Weed-plant discrimination by machine vision and artificial neural network
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DOI：
10.1006/bioe.2002.0117
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发表时间：
2002-11-01
影响因子：
5.1
通讯作者：
Jeong, JY
中科院分区：
文献类型：
--
作者：
Cho, SI;Lee, DS;Jeong, JY
开发了一种使用电荷耦合器件相机进行萝卜农场杂草检测的机器视觉系统。利用萝卜和杂草彩色图像获得的二值图像分析形状特征。使用 STEPDISC 选项选择长宽、伸长率和周长与宽度作为判别模型的重要变量。所选变量在 DISCRIM 过程中用于计算判别函数，用于将图像分类为两类之一。使用判别分析，萝卜的成功识别率为92％，杂草的成功识别率为98％。为了比判别分析更有效地识别萝卜和杂草，使用了人工神经网络（ANN）。神经网络模型100%区分萝卜和杂草。神经网络的性能得到了改进，以防止过度拟合并使用正则化方法很好地泛化。农场中萝卜的成功识别率为 93.3%，杂草的成功识别率为 93.8%。 总体而言，使用电荷耦合器件相机和 ANN 的机器视觉系统可用于检测萝卜农场中的杂草。 (C) 2002 Silsoe 研究所。由爱思唯尔科学有限公司出版。保留所有权利。
A machine vision system using a charge coupled device camera for the weed detection in a radish farm was developed. Shape features were analysed with the binary images obtained from colour images of radish and weeds. Aspect, elongation and perimeter to broadness were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using the discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds.To recognise radish and weeds more effectively than the discriminant analysis, an artificial neural network (ANN) was used. The neural network model distinguished the radish from the weeds with 100%. The performance of the neural networks was improved to prevent overfitting and to generalise well using a regularisation method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds.As a whole, the machine vision system using the charge coupled device camera with the ANN was useful to detect weeds in the radish farms. (C) 2002 Silsoe Research Institute. Published by Elsevier Science Ltd. All rights reserved.