Online detection of broken and impurity rates in half-feed peanut combine harvesters based on improved YOLOv8-Seg

Abstract

The broken and impurity rates are key indicators for evaluating the operational quality of peanut combine harvesters. In half-feed combine harvesters, the operating components are highly integrated, and various working parameters influence the final harvest quality. Real-time monitoring of the broken and impurity rates enables timely adjustment of these parameters to achieve optimal harvesting performance and improved efficiency. To address the current lack of suitable online detection devices for half-feed peanut combine harvesters, a lightweight device was developed for real-time detection of broken and impurity rates. By optimizing the sampling mechanism, continuous collection of harvested material is realized. An improved YOLOv8n-Seg model, incorporating multi-scale feature fusion, task decomposition, deformable convolution, and attention mechanisms, was proposed to enhance detection capabilities based on the composition of the materials. The network adaptively focuses on key regions, improving segmentation accuracy and adaptability under complex conditions. Lightweight modules make the model easy to deploy and reduce computational demands. Field test results show that at a machine speed of 0.4 m/s, the model achieves better detection performance, with errors of 3.97 % and 3.72 % for broken and impurity rates, respectively. At 0.8 m/s, the average relative errors increase to 7.69 % and 7.59 %, respectively. Deploying the monitoring system on a display terminal enables detection updates every 2 s on average, supporting timely parameter adjustments by the operator or an automated control system, thereby enhancing the quality and efficiency of peanut harvesting and reducing losses.