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

Abstract

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 mAP50:95 compared to YOLOv5n. For individual cottonseed image classification, the accuracy, precision, recall, and F1 scores of our two-stage deep learning model are 97.34 %, 97.7 %, 97.3 %, and 97.3 %, respectively. The gradient-weighted class activation mapping (Grad-CAM) algorithm was then used to visually explain the model’s classification mechanism. Moreover, our algorithm demonstrates superior performance compared to six commonly used classification algorithms, including ResNet-18, ResNet-50, AlexNet, GoogleNet, VGG-16, and VGG-19, achieving a notable 1.65 % increase in accuracy over the best-performing algorithm among them. We then compared its performance with four state-of-the-art (SOTA) cottonseed damage classification methods. The findings demonstrate the potential for this design to advance the development of non-destructive seed damage detection.

Plant phenotyping relevance

綿実の損傷状態という植物器官の表現型を画像から分類・検出する深層学習手法を開発し、精度比較・検証しており、表現型取得・抽出法が研究の中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.