Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis

苋菜 线性判别分析 人工神经网络 鉴定(生物学) 数学 模式识别(心理学) 人工智能 统计 植物 生物 计算机科学 农学
作者
Alireza Bagheri,L. Eghbali,R Sadrabadi Haghighi
出处
期刊:The Journal of Agricultural Science [Cambridge University Press]
卷期号:157 (04): 333-341 被引量:5
标识
DOI:10.1017/s0021859619000649
摘要

Abstract The current study was conducted in 2013 to identify the seeds of three species of Amaranthus , Amaranthus viridis L., Amaranthus retroflexus L. and Amaranthus albus L., by using the artificial neural network (ANN) and canonical discriminant analysis (CDA) methods. To begin with, photographs were taken of the seeds and 13 morphological characteristics of each seed extracted as predictor variables. Backward regression was used to find the most influential variables and seven variables were derived. Thus, predictor variables were divided into two sets of 13 and seven morphological characteristics. The results showed that the recognition accuracy of the ANN made using 13 and seven predictor variables was 81.1 and 80.3%, respectively. Meanwhile, recognition accuracy of the CDA using the seven and 13 predictor variables was 74.0 and 75.7%, respectively. Therefore, in comparison to CDA, ANN showed higher identification accuracy; however, the difference was not statistically significant. Identification accuracy for A. retroflexus was higher using the CDA method than ANN, while the ANN method had higher recognition accuracy for A. viridis than CDA. In addition, use of 13 predictor variables yielded a greater identification accuracy than seven. The results of the current study showed that using seed morphological characteristics extracted by computer vision could be effective for reliable identification of the similar seeds of Amaranthus species.

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