基于决策融合的苹果分级检测关键技术研究

提出了一种判别树和改进支持向量机决策融合的苹果分级方法。采用判别树分类方法根据果径、缺陷区域、色泽等进行分类,采用粒子群对支持向量机分类模型进行优化,根据果形、纹理和成熟度等高维特征进行分类,使用核主成分分析法降低维度,并引入决策融合的概念,结合单一特征对样本等级进行综合评估。结果表明,该方法是切实可行的,其分类准确性为98%以上,可用于苹果的有效分级。

Study on key technologies for apple grading detection based on decision fusion method

An apple grading method based on decision fusion of discriminant tree and improved support vector machine was proposed. The method of discriminant tree classification was used to classify fruit diameter, defect area and color, and the particle swarm optimization (PSO) was used to optimize the SVM classification model. The high dimensional features, such as fruit shape, texture and maturity, were used to classify, and the kernel principal component analysis (KPCA) was used to reduce the dimension. While, the concept of decision fusion was introduced to comprehensively evaluate the sample level combined with single feature. The results showed that the method was feasible, and its classification accuracy was more than 98%, which can be used for apple grading effectively.