Authors

Abstract

In order to improve the large-scale production efficiency of corn and realize the intellectualization and automation of corn seed metering technology, it is necessary to combine modern computer technology with intelligent algorithm to establish a feasible model suitable for corn seed metering device. In this paper, watershed algorithm and EDEM (EM Solutions EDEM) algorithm are used to establish an efficient corn particle recognition model. Watershed algorithm is used for image matching and recognition, EDEM algorithm is used for simulation and processing of corn particles. Twenty corn seeds were selected, and the proportion and volume fraction of seeds with different shapes were calculated by using the model. The parameters needed for simulation were calibrated to verify the reliability of corn sowing accuracy. Through the credibility evaluation of RTM (Resin Transfer Moulding) model in maize seed metering model, it can be seen that the model has credibility, and the variance test result P = 0.662 > 0.10 shows that the credibility of the model meets the requirements. The results show that the model can be applied to the large-scale production of corn seed metering device, greatly improve the production efficiency, has high reliability, and is worthy of practical application and promotion. In this paper, the model construction and Simulation of corn planter based on EDEM are deeply studied and analysed, and the related processes are improved, so as to comprehensively improve the work efficiency of corn planter and improve the quality of planter.

Abstract in Chinese

为了提高玉米规模化的生产效率，实现玉米排种技术的智能化和自动化，需要基于现代计算机技术和智能算法结合，构建出可以应用于玉米排种器的可行模型。此次选取Watershed算法及EDEM (EM Solutions EDEM)算法来构建一个高效的玉米颗粒识别模型，通过Watershed算法进行图像的匹配和识别，利用EDEM算法进行玉米颗粒的模拟和处理操作。选取20粒玉米种子，利用该模型对玉米种子进行不同形状种子的比例及体积分数的计算，对模拟所需要的各项参数进行试验标定，并对玉米种排种的精度进行可信性检验。通过RTM (Resintransfer Molding) 模型对玉米种子排种模型的可信性评估可以看出，该模型具有可信性方差齐性检验结果为P=0.662>0.10，说明该模型的可信性满足要求。研究显示，此次提出的模型能够应用于玉米规模化的排种生产之中，能够极大提升生产效率，具有很高的可靠度，值得进行生产实践应用和推广。