用视觉系统实时监测垄沟清洁度，提升播种效率
Enhancing seeding efficiency using a computer vision system to monitor furrow quality in real-time
- 通过摄像头捕捉垄沟图像，构建分割模型识别土壤、秸秆和农机
- 量化评估清茬机性能，显著提升播种前垄沟清洁度
- 适合精准农业中播种设备优化与田间管理决策
精准农业中的有效播种受残留物堆积、低温土壤及毛发状堵塞（作物残渣被开沟器推入沟内）等问题影响，导致理想垄沟难以形成。尽管采用清茬机可缓解上述问题，但缺乏对垄沟清洁度的定量评估方法。本研究开发了一种基于计算机视觉的新方法，通过在多台气力式播种机上加装视频采集系统，实时记录清茬机作业后的垄沟状况，实现各清茬机性能的客观对比。利用采集数据训练分割模型，分析土壤、秸秆和机械部件等关键元素。基于分割结果，建立客观评价清茬机性能的量化方法。实验表明，该方法有助于优化清茬机选型，显著提升播种效率。
原文摘要 · Abstract (English)
Effective seed sowing in precision agriculture is hindered by challenges such as residue accumulation, low soil temperatures, and hair pinning (crop residue pushed in the trench by furrow opener), which obstruct optimal trench formation. Row cleaners are employed to mitigate these issues, but there is a lack of quantitative methods to assess trench cleanliness. In this study, a novel computer vision-based method was developed to evaluate row cleaner performance. Multiple air seeders were equipped with a video acquisition system to capture trench conditions after row cleaner operation, enabling an effective comparison of the performance of each row cleaner. The captured data were used to develop a segmentation model that analyzed key elements such as soil, straw, and machinery. Using the results from the segmentation model, an objective method was developed to quantify row cleaner performance. The results demonstrated the potential of this method to improve row cleaner selection and enhance seeding efficiency in precision agriculture.
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