RESEARCH ON GRADING METHOD OF PEPPER PLUG SEEDLINGS BASED ON MACHINE VISION

This study proposes a grading method using combined target detection and segmentation models to enhance grading accuracy for pepper plug seedlings. We constructed separate datasets for target detection and image segmentation of pepper plug seedlings, then trained various detection models including EfficientDet, Faster R-CNN, SSD, and YOLO-series architectures. After comprehensive evaluation, YOLOv5 achieved optimal performance with 99.5% mAP, a compact 3.8 MB model size, and rapid 5.53 ms inference time per image. To avoid the impact of the culture medium and adjacent pepper seedlings on the image, we developed an improved U-Net model incorporating an Efficient Channel Attention (ECA) mechanism, enhancing segmentation accuracy by 1.29% to 93.01% while reducing processing time by 69.7% (33.32 ms). Subsequent feature extraction analyzed lateral area, height, stem thickness, hypocotyl length, and divergence degree from segmented images. Using these extracted features, grading models were trained employing support vector machines, k-nearest neighbors, and random forests. The random forest model achieved a grading accuracy of 99.33%, validating that this method meets the accuracy requirements for pepper seedling grading.