Segmentation of Unsound Wheat Kernels Based on Improved Mask RCNN.

Abstract

The grade of wheat quality depends on the proportion of unsound kernels. Therefore, the rapid detection of unsound wheat kernels is important for wheat rating and evaluation. However, in practice, unsound kernels are hand-picked, which makes the process time-consuming and inefficient. Meanwhile, methods based on traditional image processing cannot divide adherent particles well. To solve the above problems, this paper proposed an unsound wheat kernel recognition algorithm based on an improved mask RCNN. First, we changed the feature pyramid network (FPN) to a bottom-up pyramid network to strengthen the low-level information. Then, an attention mechanism (AM) module was added between the feature extraction network and the pyramid network to improve the detection accuracy for small targets. Finally, the regional proposal network (RPN) was optimized to improve the prediction performance. Experiments showed that the improved mask RCNN algorithm could identify the unsound kernels more quickly and accurately while handling adhesion problems well. The precision and recall were 86% and 91%, respectively, and the inference time on the test set with about 200 targets for each image was 7.83 s. Additionally, we compared the improved model with other existing segmentation models, and experiments showed that our model achieved higher accuracy and performance than the other models, laying the foundation for wheat grading.

Plant phenotyping relevance

改良Mask R-CNNによる小麦粒の不健全状態の画像分割・認識手法を開発し、精度・再現率・推論時間および他モデルとの比較で技術検証しているため、植物表現型取得が中心である。

Code and data availability

The paper's wheat-kernel image dataset is stated to be available in a GitHub repository (shenranVic/wheat-detection), but that repository URL is not among the allowed URLs, so no actionable public URL can be provided. The only allowed URL in the text (labelme) is a generic annotation tool, not a paper-specific asset.

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