Timely and precise farm inspection, which involves the identification and recognition of harmful insects and diseases, is crucial for safeguarding crop production. Traditional vision-based pest recognition methods typically require extensive annotated data for each pest species and a lengthy training process. This approach is time-consuming, labor-intensive, and prone to human error. Zero-shot learning offers a potential solution by enabling pest segmentation and control without requiring explicit training data. This study supports farmers in automatically identifying ten common pests and their precise locations in real-world outdoor environments. The zero-shot pest segmentation is based on a hybrid approach combining Explainable Contrastive Language-Image Pre-training (ECLIP) and Segment-Anything (SAM). Moreover, an optimized super-resolution model and various data augmentation methods are implemented to improve the quality of the dataset. Lastly, a mask post-processing step is applied to remove highly overlapping segmented masks and noise blobs caused by the complex background. The mean Intersection over Union (mIoU) of 66.5 % on the validation set demonstrates the potential of zero-shot methods for automated pest segmentation during farm inspections. This research lays the foundation for accurate pest monitoring systems capable of adapting to new pests, ultimately improving agricultural productivity.

中文翻译：

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一种有效的零标签分割方法，用于基于智能手机的图像上的害虫监测


及时、准确的农场检查涉及有害昆虫和疾病的识别和识别，对于保障作物生产至关重要。传统的基于视觉的害虫识别方法通常需要每种害虫种类的大量注释数据和漫长的训练过程。这种方法费时、费力，而且容易出现人为错误。零样本学习提供了一种潜在的解决方案，无需明确的训练数据即可实现害虫分割和控制。这项研究支持农民自动识别十种常见害虫及其在现实户外环境中的精确位置。零样本害虫分割基于可解释对比语言图像预训练（ECLIP）和任意分割（SAM）的混合方法。此外，还实施了优化的超分辨率模型和各种数据增强方法来提高数据集的质量。最后，应用掩模后处理步骤来去除高度重叠的分段掩模和由复杂背景引起的噪声斑点。验证集上的平均交集 (mIoU) 为 66.5%，这表明零样本方法在农场检查期间自动进行害虫分割的潜力。这项研究为能够适应新害虫的准确害虫监测系统奠定了基础，最终提高了农业生产力。