Abstract
In the fully automated grafting process of watermelon seedlings, it is crucial to ensure that the scion’s cotyledons maintain a perpendicular orientation with the rootstock cotyledons. To achieve precise segmentation of watermelon scion cotyledons and accurately extract parameters, such as cotyledon orientation angles, this study introduces enhancements to the Mask2Former network, aiming to improve segmentation accuracy for watermelon scion cotyledons. Specifically, two innovative modules are designed. Taking Swin-Former as the backbone, an Optimal Feature Re-ranking (OFR) module based on the Hungarian Algorithm is devised to re-rank the feature maps obtained from the feature extraction process. Grounded in information theory, the amount of information in semantic segmentation tasks is quantified as Shannon entropy, enabling the model to perceive the information distribution of the feature maps and dynamically adjust the output features. Experimental results demonstrate that the improved model achieves mIoU, mDice, mPrecision, and mRecall scores of 97.44%, 98.70%, 98.20%, and 99.21%, respectively, greatly outperforming Mask2Former, FCNN, and DeepLabv3. Furthermore, the enhanced network exhibits superior accuracy in low signal-to-noise ratio environments, highlighting its robustness in complex scenarios. This study provides a high-precision solution for agricultural automation in the watermelon industry, contributing to the development of fully automated grafting machines.
Published Version
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