| 基于神经网络及近红外光谱的草莓成熟度快速识别方法 |
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| 引用本文： | 闫润,王新忠,张莹莹.基于神经网络及近红外光谱的草莓成熟度快速识别方法[J].安徽农业科学,2012,40(10). |
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| 作者姓名： | 闫润 王新忠 张莹莹 |
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| 作者单位： | 1. 江苏大学现代农业装备与技术省郝共建教育部重点实验室,江苏镇江212013;江苏农林职业技术学院机电工程系,江苏句容212400
2. 江苏大学现代农业装备与技术省郝共建教育部重点实验室,江苏镇江,212013 |
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| 基金项目： | 江苏省高校自然科学研究项目(09KJD210003) |
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| 摘 要： | 目的]探究快速检验草莓的成熟度、提高草莓采摘自动化水平的方法。方法]采集350～2 500 nm波段草莓的光谱信息,提取光谱的一阶导数值并进行主成分分析,取2个主成分并获得了主成分中贡献率最大的6个峰值点,将各个峰值点两侧各取5个波段点作为特征点。随机选取58个样本组成60×58的矩阵作为训练集,导入用Matlab建立的人工BP神经网络中进行训练。结果]利用测试集进行识别模型的检验,识别正确率达到93.1%。结论]利用近红外光谱对草莓成熟度进行识别是可行的。
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| 关 键 词： | 神经网络 主成分分析 近红外光谱 成熟度识别 草莓 |
Fast Recognition Method of Strawberries' Maturity Level Based on Neural Network and Near Infrared Spectra |
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| Abstract: | Objective] To explore the method for the rapid detection of strawberries' maturity level and the improvement of the automatic strawberry picking level.Method] The strawberries' spectral information around 350-2 500 nm was collected,and the first derivative of the strawberries' spectral information was drawn for carrying out a principal components analysis.The six points which contributed the largest in the principle components were obtained.The five additional feature points aside each crest feature points were picked out as main feature points,and 58 strawberries were selected randomly to made up a 60×58 array as training set.The array was imported to BP neural network which was built of Matlab for training.Result] To test the neural network model by the strawberries samples of test set,the distinguishing rate could reach up to 93.1%.Conclusion] To identify the maturity degree of strawberries by near infrared spectra is practicable. |
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| Keywords: | Neural network Principal components analytic Near infrared spectra Recognition of maturity level Strawberry |
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