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植物CaFo:基于基础模型的高效的几次射击植物疾病识别方法

Xue Jiang¹, Jiashi Wang¹, Kai Xie¹

¹College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China.

Plant phenomics (Washington, D.C.)

|December 19, 2025

概括

本研究介绍了PlantCaFo,这是一个高效的几次射击植物疾病识别模型,利用基础模型. PlantCaFo在公共和现实数据集上实现了高精度,改善了农业诊断.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
机器学习机器学习

背景情况:

传统的植物疾病识别方法昂贵,样本稀缺.
短期学习和转移学习提供解决方案,但往往需要特定领域的预培训.
基础模型在少量学习场景中显示出前景.

研究的目的:

探索基础模型的潜力,以有效地识别几次射击植物疾病.
提出一个端到端的模型,PlantCaFo,整合来自多个预训练模型的知识.
为了应对数据收集的挑战和植物疾病识别中的样本稀缺性.

主要方法:

开发了PlantCaFo,这是一个基于基础模型的高效的几次射击植物疾病识别模型.
设计了一种轻量级的扩展上下文适配器 (DCon-Adapter),用于学习新知识.
使用重量分解矩阵 (WDM) 来更新端到端网络中的文本重量.

主要成果:

在38路16拍设置中,在PlantVillage数据集上获得了93.53%的准确性.
与Cassava数据集 (自然环境图像) 的基线相比,精度提高了6.80%.
在非分发数据集上证明了显著的准确性增加,验证了概括.

结论:

与现有的模型相比,PlantCaFo提供了一种优越的方法来识别少数射击植物疾病.
基础模型可以有效地适应农业应用,如植物疾病识别.
拟议的DCon适配器和WDM有助于高效的知识集成和适应.

关键词:

有效的运作有效的运作有几次射击学习学习.基金会模型基金会模型植物病的识别植物病的识别

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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.