智能手机图像害虫监测的高效零标记分割方法

IF 4.5 1区 农林科学 Q1 AGRONOMY
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引用次数: 0

摘要

及时、精确的农场检查,包括识别有害昆虫和疾病,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为每种害虫提供大量注释数据和漫长的训练过程。这种方法耗时、耗力,而且容易出现人为错误。零镜头学习提供了一种潜在的解决方案,它无需明确的训练数据,就能实现害虫分割和控制。本研究支持农民自动识别十种常见害虫及其在真实室外环境中的精确位置。零镜头害虫分割基于一种混合方法,该方法结合了可解释对比语言-图像预训练(ECLIP)和任意分段(SAM)。此外,还采用了优化的超分辨率模型和各种数据增强方法,以提高数据集的质量。最后,应用掩膜后处理步骤来去除高度重叠的分段掩膜和复杂背景造成的噪点。验证集上的平均交集大于联合度(mIoU)为 66.5%,这证明了零镜头方法在农场检查期间进行自动害虫分割的潜力。这项研究为能够适应新害虫的精确害虫监测系统奠定了基础,最终将提高农业生产率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient zero-labeling segmentation approach for pest monitoring on smartphone-based images

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.

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来源期刊
European Journal of Agronomy
European Journal of Agronomy 农林科学-农艺学
CiteScore
8.30
自引率
7.70%
发文量
187
审稿时长
4.5 months
期刊介绍: The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics: crop physiology crop production and management including irrigation, fertilization and soil management agroclimatology and modelling plant-soil relationships crop quality and post-harvest physiology farming and cropping systems agroecosystems and the environment crop-weed interactions and management organic farming horticultural crops papers from the European Society for Agronomy bi-annual meetings In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.
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