RSHRNet: Improved HRNet-based semantic segmentation for UAV rice seedling images in mechanical transplanting quality assessment

Abstract

Rice seedling morphological identification is crucial for assessing the quality of mechanical transplanting, a process that has traditionally relied on subjective and inefficient manual inspections. To address this challenge, this paper introduces a non-contact quality assessment approach for rice mechanical transplanting based on unmanned aerial vehicle (UAV) imagery. Specifically, we propose a semantic segmentation model for rice seedlings based on an improved HRNet architecture, named RSHRNet. Leveraging UAV imagery as the data source, the methodology employs HRNet as the backbone network, facilitating the acquisition of high-resolution feature information through parallel interactions. Then, the object-contextual representations (OCR) module and coordinate attention mechanism are subsequently introduced to comprehensively aggregate contextual feature information and enhance the model’s ability to extract spatial positional information. This approach helps to resolve issues of blurry edges and misclassification related to rice seedling target segmentation. Finally, to validate the effectiveness and advancements of the proposed method, comparative analyses are conducted against classical segmentation algorithms, followed by practical tests. Experimental results demonstrate the outstanding performance of the proposed RSHRNet in the segmentation of rice seedlings captured by UAVs, providing a solid foundation for the evaluation of mechanical transplanting quality.

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