稻穗结构图像特征与籽粒数相关关系分析
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高等学校博士学科点专项科研基金资助项目(20130097110042)、中央高校基本科研业务费专项资金资助项目(KYZ201161)和南京农业大学工学院引进人才科研启动基金资助项目(RCQD11—01)


Relative Analysis between Image Characteristics of Panicle Structure and Spikelet Number
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    摘要:

    针对人工测量低效,重复性差且无法同时获取多个参数的问题,提出稻穗结构图像特征测量方法。首先人工测量稻穗一次枝梗长度和每穗籽粒数,发现二者具有显著相关性;采用图像处理方法提取稻穗图像面积、一次枝梗长度特征,并分别分析二者与籽粒数的相关关系。试验结果表明,面积、一次枝梗长度特征与籽粒数的相关系数在0.90以上,预测籽粒数的最大误差平均值为7.90%,说明稻穗形态特征的图像提取方法可行有效,面积、一次枝梗长度均能用来表达或替代稻穗籽粒数特征。

    Abstract:

    Characteristics extraction method of rice panicle images was proposed to solve the traditional measurement problems, such as inefficiency, worse repeatability, obtaining multi-parameters difficultly. There was significant correlation between primary branch length and spikelet number obtaining by manual measurement. Consequently, rice panicle was spread out and images were captured. Image characteristics were extracted using some image processing operations, including area, primary branch length, and panicle skeleton. Experimental result showed that the correlation coefficients were up to 0.90 between image characteristics and spikelet number, and the average predicting error of the model was 7.90%. Consequently, characteristics extraction method of rice panicle images was effective and feasible. The area and primary branch length can be perfect expression and substitute for spikelet number.

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赵三琴,李毅念,丁为民,吕俊逸,王心怡.稻穗结构图像特征与籽粒数相关关系分析[J].农业机械学报,2014,45(12):323-328. Zhao Sanqin, Li Yinian, Ding Weimin, Lü Junyi, Wang Xinyi. Relative Analysis between Image Characteristics of Panicle Structure and Spikelet Number[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(12):323-328.

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  • 收稿日期:2014-01-15
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  • 在线发布日期: 2014-12-10
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