A Corn Ear Mass Prediction Method Based on TS-YOLO and Random Forest

Abstract

Accurate estimation of corn ear mass is an essential component of intelligent harvesting. To achieve automated mass measurement of large-scale corn ears, this study developed a dual-view prediction platform and proposed a corn ear mass prediction method based on TS-YOLO and Random Forest. First, a lightweight TS-YOLO instance segmentation model was developed using YOLOv11n-seg as the baseline network to extract morphological features. By incorporating the MStarnet multi-scale feature extraction structure, LSKA attention mechanism, and LADH detection head, the proposed model enhanced feature representation of corn ears and damaged regions while reducing model complexity. Compared with the baseline model, TS-YOLO reduced the number of parameters and computational cost by 15.04% and 11.76%, respectively, and achieved an mAP@.5 of 0.959. Subsequently, a Random Forest regression model was established by integrating morphological features with corn variety and moisture content information. The Random Forest model yielded an R2 of 0.894, with corresponding RMSE and MAE values of 18.19 g and 13.97 g, respectively. Across corn varieties and moisture levels, the mean prediction error was 3.86%, indicating consistent and reliable performance under varying sample conditions. The proposed approach enables automated corn ear mass estimation and provides support for intelligent corn ear inspection.