Liu Teng, Jin Xiaojun, Han Kang, He Feiyu, Wang Jinxu, Chen Xin, Kong Xiaotong, Yu Jialin
Peking University Institute of Advanced Agricultural Sciences/Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China.
Department of Computer Science, Duke University, Durham, NC, USA.
Pest Manag Sci. 2025 Mar;81(3):1512-1528. doi: 10.1002/ps.8554. Epub 2024 Nov 25.
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.
The proposed method employs a semantic segmentation model to generate a mask of corn (Zea mays L.) crops, identifying all green plant pixels outside the mask as weeds. This indirect segmentation approach reduces model complexity by avoiding the need for direct detection of diverse weed species. To enhance real-time performance, the semantic segmentation model was optimized through knowledge distillation, resulting in a faster, lighter-weight inference. Experimental results demonstrated that the DeepLabV3+ model, after applying knowledge distillation, achieved an average accuracy (aAcc) exceeding 99.5% and a mean intersection over union (mIoU) across all categories above 95.5%. Furthermore, the model's operating speed surpassed 34 frames per second (FPS).
This study introduces a novel method that accurately segments crop pixels to form a mask, identifying vegetation outside this mask as weeds. By focusing on crop segmentation, the method avoids the complexity associated with diverse weed species, varying densities, and different growth stages. This approach offers a practical and efficient solution to facilitate the training of effective computer vision models for precision weed detection and control. © 2024 Society of Chemical Industry.
在农田中进行可靠、快速且准确的杂草检测对于精准杂草管理至关重要，但由于不同田地中存在多种杂草物种，这一任务仍然具有挑战性。尽管先前的研究已经开发出用于直接杂草检测的深度学习模型，但创建一个涵盖所有可能杂草物种、生态类型和生长阶段的训练数据集实际上是不可行的。本研究提出了一种将语义分割与图像处理相结合来检测杂草的新方法。其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，从而简化杂草检测过程。
所提出的方法采用语义分割模型生成玉米（Zea mays L.）作物的掩膜，将掩膜外的所有绿色植物像素识别为杂草。这种间接分割方法通过避免直接检测多种杂草物种来降低模型复杂性。为了提高实时性能，通过知识蒸馏对语义分割模型进行了优化，从而实现了更快、更轻量级的推理。实验结果表明，应用知识蒸馏后的DeepLabV3+模型平均准确率（aAcc）超过99.5%，所有类别的平均交并比（mIoU）高于95.5%。此外，该模型的运行速度超过每秒34帧（FPS）。
本研究引入了一种新方法，该方法能够准确分割作物像素以形成掩膜，并将该掩膜外的植被识别为杂草。通过专注于作物分割，该方法避免了与多种杂草物种、不同密度和不同生长阶段相关的复杂性。这种方法提供了一种实用且高效的解决方案，有助于训练用于精准杂草检测和控制的有效计算机视觉模型。© 2024化学工业协会。
