Reliable, fast, and accurate weed detection in farmland is crucial for precision weed management but remains challenging due to the diverse weed species present across different fields. While deep learning models for direct weed detection have been developed in previous studies, creating a training dataset that encompasses all possible weed species, ecotypes, and growth stages is practically unfeasible. This study proposes a novel approach to detect weeds by integrating semantic segmentation with image processing. The primary aim is to simplify the weed detection process by segmenting crop pixels and identifying all vegetation outside the crop mask as weeds.

中文翻译：

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玉米杂草检测的语义分割


可靠、快速和准确的农田杂草检测对于精确的杂草管理至关重要，但由于不同田地中存在多种杂草，因此仍然具有挑战性。虽然在以前的研究中已经开发了用于直接杂草检测的深度学习模型，但创建一个包含所有可能的杂草种类、生态类型和生长阶段的训练数据集实际上是不可行的。本研究提出了一种通过将语义分割与图像处理相结合来检测杂草的新方法。主要目的是通过分割作物像素并将作物掩膜外的所有植被识别为杂草来简化杂草检测过程。