Evaluation of segmentation and semi‐supervised learning for spotted spurge recognition in bermudagrass turf

Abstract

Diverse datasets are crucial for training machine learning‐based weed recognition models. However, annotating (i.e., labeling) images can be laborious and time‐consuming. Choice of annotation method and training approach not only affects the overall model effectiveness but also its minimum training data requirements and development pace. Segmentation and semi‐supervised learning (SSL) may offer performance or training enhancements. This study evaluated (1) segmentation against object detection in spotted spurge [Chamaesyce maculata (L.) Small] recognition in Latitude 36 hybrid bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] maintained as a golf course fairway and (2) the potential of two‐step SSL‐based training procedure using data labeled both manually and automatically with a pre‐trained model to expedite the model development. The architecture used across this research was You Only Look Once version 8 (YOLOv8) employing nano, small, and medium variants. All models were trained with a dataset restricted to 1200 training and 300 validation images. Both annotation methods resulted in adequate spotted spurge identification, as evidenced with >0.60 (>0.50 acceptability threshold) medium average precision at intersection over union threshold of 0.50 (mAP@50). Although the difference was minimal, object detection performed superior to segmentation. The two‐step training effectively accelerated image annotation while preserving or improving (with 8:4 and 6:6 splits between manually and auto‐labeled data) object detection performance. Segmentation tolerated only 10:2 split and exhibited increased sensitivity to declining dataset quality proportionally to the increase in auto‐labeled images in the final training dataset. Findings show a two‐step SSL‐based training procedure expedites annotation, enhancing model development efficiency.