Abstract The cleaning of rice on a combine harvester is a complex process, which leads to differences in impurity ratio of harvested grain, and the impurity ratio is one of the key criteria for the assessment of performance of a combine harvester. Combine operators usually optimize parameter settings only once for harvesting each crop because of time pressure, and therefore differences in site- and temporal-specific conditions are neglected. In this paper, to offer combine operators the opportunity to make better management decision, a machine vision method for grain impurity monitoring of a rice combine harvester in real time was proposed, and the classification of kernel and impurity particles using decision tree algorithm was presented. To obtain images of high quality during harvesting, the structure of the sampling device depending on the working properties in grain bin was designed, the illumination and installation of the light source were optimized, and finally lateral lighting system was constructed. To monitor and recognize grains and impurities, the morphological features of the particles extracted from the images were acquired. The selected 6 features (A1-A6), including area, perimeter, maximal feret diameter, elongation factor, compactness factor and Heywood circularity factor, were fed to the decision tree algorithm for classification. Output of the algorithm, a visualized tree, was used to classify the particles labeled in the binary image. The decision tree provided a classification accuracy of about 76% for the given training data set extracted from the captured images. From the experimental results, it is suggested that the method of monitoring the impurity ratio of harvested grains based on decision tree algorithm using image processing can be recommended as the basis of parameter optimization of combine harvesters.

中文翻译：

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基于图像处理和决策树算法的水稻联合收割机谷物杂质实时检测

摘要 联合收割机的稻谷清理是一个复杂的过程，导致收获的谷物杂质率存在差异，杂质率是评价联合收割机性能的关键标准之一。由于时间压力，联合收割机操作员通常只优化一次参数设置以收获每种作物，因此忽略了地点和时间特定条件的差异。为了给联合收割机操作人员提供更好的管理决策机会，提出了一种用于水稻联合收割机谷物杂质实时监测的机器视觉方法，并提出了利用决策树算法对籽粒和杂质颗粒进行分类的方法。为了在收获期间获得高质量的图像，根据粮仓内的工作特性设计了取样装置的结构，优化了光源的照明和安装，最后构建了侧向照明系统。为了监测和识别颗粒和杂质，获取了从图像中提取的颗粒的形态特征。选定的6个特征（A1-A6），包括面积、周长、最大feret直径、伸长因子、紧密度因子和Heywood圆度因子，被输入决策树算法进行分类。该算法的输出是一个可视化树，用于对二值图像中标记的粒子进行分类。对于从捕获的图像中提取的给定训练数据集，决策树提供了大约 76% 的分类准确率。从实验结果来看，