Improved two-stage deep learning algorithm and lightweight YOLOv5n for classifying cottonseed damage

作者：Tingshu He, Fan Wu, Lori Unruh Snyder, Jean Cheng, Evelynn Wilcox, Lirong Xiang · 发表于：Computers and Electronics in Agriculture · 年份：2025 · DOI：10.1016/j.compag.2025.110042 · 被引用次数：5 · 研究领域：Smart Agriculture and AI、Spectroscopy and Chemometric Analyses、Industrial Vision Systems and Defect Detection

• The first systematic and precise definition of various categories of cottonseed defects. • An innovative YOLOv5n-Swin Transformer model with a two-stage classification for cottonseed damage detection. • Our detection model is 30.11% smaller than YOLOv5n and classification network achieves 97.34% classification accuracy. With a rich historical background, the US cotton industry consistently maintains its position as one of the leading global producers. Due to the direct correlation between cottonseed quality and germination rate, conducting non-destructive testing to identify defects in cottonseeds becomes important to optimize yield performance. In this study, we propose an objective method for detecting cottonseed defects which classifies cottonseeds into four categories (Normal, Pinhole, Damage, and Very Damaged) and fourteen subcategories (N, R, C, RH, EH, CH, R Cut, C Cut, RV, CV, RH Expose, EH Expose, CH Expose, and V). Leveraging our customized cottonseed image dataset, we introduce a cottonseed defect detection and classification method based on a lightweight YOLOv5n model enhanced with Swin Transformer and an improved two-stage deep learning classification model. For cottonseed detection, our method achieves a 30.11 % reduction in model size and a 7.7 % increase in m A P 50 : 95 compared to YOLOv5n. For individual cottonseed image classification, the accuracy, precision, recall, and F 1 scores of our two-stage deep learning model are 97.34 %, 97.7 %, 97.3 %, and 97....