Research on wheat broken rate and impurity rate detection method based on DeepLab-EDA model and system construction

The broken rate and impurity rate of wheat are important indicators for assessing the quality of combine harvester operations. In view of the overlapping, occlusion and dense adhesion between the scattered grains during the operation of the combine harvester, it is difficult to obtain the grain crushing characteristics and impurity mass, which leads to low detection accuracy. In this paper, a method for detecting wheat broken rate and impurity rate based on DeepLab-EDA semantic segmentation model was proposed, and a detection system was built. In the detection system, an image acquisition device was designed and developed based on the principle of electromagnetic vibration, and the deep learning model was deployed in the embedded processor. Through the human–computer interaction interface design, the online processing and analysis of wheat image data and the display of the detection results of broken rate and impurity rate were realized. Comparative experiments with traditional semantic segmentation models showed that the MIoU, MP and MR of the DeepLab-EDA model were 89.41 %, 95.97 % and 94.83 %, respectively, representing improvements of 9.94%, 7.41%, and 7.52% over the baseline model, and indicating a significant enhancement in the accurate identification and segmentation of broken grain and impurities. Based on this, indoor group matching experiments were conducted with three groups of broken rate and impurity rate levels set at 0.5%, 1.5%, and 2.5%, showing the average errors of 7.54% and 6.30% for broken rate and impurity rate detection systems, respectively. Furthermore, the detection device was installed under the grain outlet of the GM80 combine harvester for field experiments, which showed average errors of 13.32% and 9.77% for wheat broken rate and impurity rate, respectively. The effectiveness and accuracy of the wheat broken rate and impurity rate detection system meet the requirements, which can provide a data basis for intelligent control of combine harvester operation parameters by the operator.