李浩光,李卫军,覃鸿,于丽娜,于云华,逄燕.基于栈式自编码神经网络的包衣单籽粒玉米品种识别[J].农业机械学报,2017,48(s1):422-428.
LI Haoguang,LI Weijun,QIN Hong,YU Li’na,YU Yunhua,PANG Yan.Varietal Identification for Single Maize Seed Based on Stacked Auto Encoder Neural Network[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(s1):422-428.
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基于栈式自编码神经网络的包衣单籽粒玉米品种识别   [下载全文]
Varietal Identification for Single Maize Seed Based on Stacked Auto Encoder Neural Network   [Download Pdf][in English]
投稿时间:2017-07-10  
DOI:10.6041/j.issn.1000-1298.2017.S0.065
中文关键词:  玉米  品种识别  栈式自编码神经网络  种衣剂
基金项目:国家重大科学仪器设备开发专项(2014YQ470377)和中国石油大学胜利学院科技计划项目(KY2017006、KY2015011)
作者单位
李浩光 中国科学院半导体研究所
中国石油大学 
李卫军 中国科学院半导体研究所 
覃鸿 中国科学院半导体研究所 
于丽娜 中国科学院半导体研究所 
于云华 中国石油大学 
逄燕 中国石油大学 
中文摘要:常规近红外定性识别研究中,玉米籽粒为表皮裸露状态,未经种衣剂覆盖处理,但是在实际农业生产中,为抵御病虫害侵袭,提高玉米种子发芽率,达到保产增产的功效,玉米种子常需经种衣剂包裹处理。玉米种衣剂的类型多样,对近红外光谱具有一定的吸收,因此种衣剂对近红外定性识别具有干扰作用。本文针对种衣剂对玉米品种识别准确性影响的问题,提出了一种基于栈式自编码神经网络(SAE)的近红外光谱定性建模方法。首先采用无种衣剂玉米籽粒光谱作为训练集,通过栈式自编码无监督学习算法与softmax分类器构建栈式自编码网络定性分析模型,再利用所建模型对有种衣剂玉米籽粒进行品种识别。实验结果表明,基于SAE的建模方法能够将种衣剂对玉米籽粒识别率的影响降低至3%以内。
LI Haoguang  LI Weijun  QIN Hong  YU Li’na  YU Yunhua  PANG Yan
Institute of Semiconductors, Chinese Academy of Sciences;China University of Petroleum,Institute of Semiconductors, Chinese Academy of Sciences,Institute of Semiconductors, Chinese Academy of Sciences,Institute of Semiconductors, Chinese Academy of Sciences,China University of Petroleum and China University of Petroleum
Key Words:maize  varietal authenticity identification  stack auto encoder neural network  seed coating
Abstract:In the conventional near infrared qualitative identification, the maize seed kernel epidermis was not treated with seed coating agent. However, in the actual agricultural production, in order to resist the invasion of diseases and insect pests, improve the germination rate, and achieve the effect of maintaining and increasing yield, maize seeds often need to be coated with seed coating agents. In reality, on the market, it is usually necessary to model maize seed kernels without seed coating to identify the ones with seed coating, so as to achieve the purpose of cracking down fake and shoddy products. The maize seeds coating usually consist of a mixture of insecticides, fungicides, fertilizer, plant growth regulators and other ingredients. Their types are diverse and the components are different. These components contain hydrogen group organic compounds, which have certain absorption to near infrared spectrum. Therefore, the seed coating agent had an interference effect on near infrared spectroscopy qualitative identification, which reduced the performance of some conventional shallow learning model. According to the effects of seed coating on maize variety authenticity identification accuracy, a method of near infrared spectroscopy qualitative modeling based on stacked autoencoder (SAE) neural networks has been proposed. Firstly, taking maize seed spectrum without seed coating agent as the training set, a qualitative analysis model was constructed through SAE unsupervised learning algorithm and Softmax classifier. Then, based on this model, the authenticity of maize seeds with seed coating agents was identified. The experimental results showed that, by using the method based on SAE, the effect of seed coating on maize varietal authenticity recognition rate was controlled within 3%.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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