草地学报

›› 2014, Vol. 22 ›› Issue (2): 426-428.DOI: 10.11733/j.issn.1007-0435.2014.02.034

• 研究简报 • 上一篇    下一篇

基于MATLAB的苜蓿草品质分级系统

陈积山1, 朱瑞芬1, 张月学1, 许瑞轩2, 张英俊2   

  1. 1. 黑龙江农科院草业研究所, 黑龙江 哈尔滨 150086;
    2. 中国农业大学动物科技学院, 北京 100193
  • 收稿日期:2013-05-27 修回日期:2013-08-23 出版日期:2014-04-15 发布日期:2014-04-21
  • 通讯作者: 张英俊
  • 作者简介:陈积山（1979-），男，甘肃古浪人，助理研究员，硕士，主要从事牧草栽培及草地管理，E-mail：cjshlj@163.com
  • 基金资助:
    “十二五”国家支撑计划“草业及草原可持续发展关键技术研究与示范”（2011BAD17B00）；现代农业产业技术体系建设专项资金（CARS-35）资助

Alfalfa Quality Classification System Based on MATLAB

CHEN Ji-shan1, ZHU Rui-fen1, ZHANG Yue-xue1, XU Rui-xuan2, ZHANG Ying-jun2   

  1. 1. Institute of Pratacultural Science, Heilongjiang Academy of Agricultural Science, Harbin, Heilongjiang Province 150086, China;
    2. China Agricultural University, Beijing 100193, China
  • Received:2013-05-27 Revised:2013-08-23 Online:2014-04-15 Published:2014-04-21

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摘要： 为了客观评估苜蓿（Medicago sativa L.）草品质的等级，采用MATLAB中BP人工神经网络，利用苜蓿粗蛋白（CP）、中性洗涤纤维（NDF）、酸性洗涤纤维（ADF）、随意可消化量（DDM）、采食量（DMI）参数建立BP神经网络模型。通过200个苜蓿样本进行网络训练，并采用不同的BP神经网络隐含层的传递函数和隐含层神经元数量，获得最优BP神经网络模型。结果表明：在5个特征参数指标下，仿真评价苜蓿草品质等级的准确率达到99.6%，与人工评估结果相比，仿真结果更符合苜蓿草品质的客观现实。在此基础上，介绍已经开发建立的我国首个苜蓿草品质分级系统，有助于未来在苜蓿草市场中发挥其等级评定的应用潜力。

关键词: 苜蓿, 品质, 分级系统, MATLAB

Abstract: In order to evaluate alfalfa (Medicago sativa L.) grade objectively, BP neural network model was established using CP, NDF, ADF, DDM and DMI based on the BP artificial neural network of MATLAB. The neural network model of BP was trained by 200 alfalfa samples. The simulation result indicated that BP neural network model was more suitable to evaluate alfalfa grade and the rate of accuracy was up to 99.6% compared with the artificial result under the characteristic parameter of alfalfa. Therefore, alfalfa quality classification system established in this paper had potential to evaluate alfalfa grade in alfalfa market.

Key words: Alfalfa, Quality, Classification system, MATLAB

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