摘要
为实现番茄果实质量的预测,提出一种应用计算机视觉技术对番茄果实质量进行自动识别的方法。采用Matlab平台构建算法,利用数学形态运算及图像局部性质运算等构成的识别算法,对番茄样本图像进行预处理,提取番茄图像投影面积、轮廓周长、最大内接圆直径和最小外接圆直径外部特征参数,分别建立番茄果实质量与4个特征参数之间的一元线性、二阶多项式、幂指和多元线性预测模型。试验结果显示:采用多元线性预测模型预测结果最佳,其决定系数为0.926 7,标准差为4.32;利用检验样本对预测模型进行验证,预测果实质量与实际果实质量的绝对误差均值为3.260g,相对误差均值为1.535%。研究结果表明,基于计算机视觉技术外部特征信息的番茄果实质量预测方法是可行的。
In order to classify tomatoes nondestructively, this paper put forward method, an image technology to predict tomato weight automatically. The algorithm was based on Matlab platform, using mathematical morphological operation and image local property operation to establish identification algo- rithm for the tomato. Linear model, second order polynomial model, power model were established through analyzing the relations between tomato external parameters and weight. Weight prediction mod- el was established through regression analysis of the external characteristics. The experiment results showed that the weight was highly related to its area, perimeter, maximum inscribed circle diameter and minimum circumscribed circle diameter. Test results showed that the multivariate linear prediction model was the best. Its determination coefficient （R2） was 0. 926 7, the standard deviation （SE） was 4. 32. Its mean relative error was 1. 535%, and the mean absolute error was 3. 260 g. The experiment results show that the program can quickly predict tomato weight through tomato external features.
出处
《华中农业大学学报》
CAS
CSCD
北大核心
2013年第6期144-148,共5页
Journal of Huazhong Agricultural University
基金
湖北省科技厅研究与开发自主项目(2009BB013)
关键词
番茄
外部特征
计算机视觉
预测模型
tomato
external characteristic
computer vision
prediction model
