摘要
营养元素是影响大豆品质和产量的重要因素,营养元素过量或缺乏均会对大豆产生严重影响。传统的营养元素诊断方法主要依靠人的主观经验,容易出现误诊、漏诊等情况,而传统的化学分析技术又会出现破坏植株、测试手段繁琐、周期长等缺点。文章通过分析大豆在营养元素过量或缺乏的不同情况下的植物特性,构建了基于模式识别的营养元素智能诊断系统,并提出了应用计算机视觉技术提取大豆形态特征,把拍摄的图像利用适当的方法进行图像分割、增强、平滑、滤波等处理,利用图像处理算法分割出叶脉、叶肉、叶缘,识别出颜色、纹理等形态特征,分析元素失衡时的颜色及纹理在叶片的不同部位表现,为今后进一步利用模式识别诊断大豆营养元素提供了发展方向。
Nutrition element is an important factor that affects quality and yield of soybean. Such as excessive or lacking nutrients will generate serious impacts on soybean. The traditional diagnostic methods of nutrients that mainly rely on subjective experience, so it is prone to causing misdiagnosis and missing diagnosis etc. The tradition chemical analysis technology may destroy individual plant, and its testing means is miscellaneous and tdvial, which needs the long period and so on. The paper by analyzed the characters of the soybean nutrition elements form a nutrition element intelligence diagnosing system that based on the pattern recognition. According to soybean＇s structural difference in excessive or lacking nutrients state, and this paper also established nutrients intelligent diagnostic system which based on pattern recognition, and put forward the method to extract soybean structural features by a series of image processes （image segmentation, enhancing, smoothing and filtering etc.）. The method could divide up leaf veins, mesophyll and leaf margin and identify the color, texture and many other structural features by image processing algorithms. By analyzing the elements in different conditions of color and texture in different parts of soybeans during the unbalance period, which provided the development direction of soybean nutrition element intelligence diagnosing system based on pattern recognition.
出处
《东北农业大学学报》
CAS
CSCD
北大核心
2009年第7期106-110,共5页
Journal of Northeast Agricultural University
基金
中国博士后科研基金(20080430886)
黑龙江省出站博士后科研启动基金
东北农业大学博士启动基金
关键词
模式识别
大豆
营养元素
智能诊断系统
pattern recognition
soybean
nutrition element
intelligence diagnosing system
