Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm

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.