发表机构
Technical University of Denmark (DTU); Pioneer Centre for Artificial Intelligence（丹麦技术大学（DTU）; 人工智能先锋中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对农业计算机视觉依赖昂贵标注与模型适配成本高的问题，构建含17种公开资源的AgriField-40K数据集，提出参数高效的AgriMAE方法，可少用9倍可训练参数实现与全微调相当甚至更优的下游性能，为农业视觉持续预训练提供实用资源。
AI 中文摘要
基于田间的农业计算机视觉对精准农业至关重要，但该领域很大程度上依赖昂贵的标注工作，且对大型预训练模型的适配成本高昂。我们推出AgriField-40K，这是一个以田间为核心的数据集，由17种公开资源整理而成，涵盖多样化的作物、杂草及田间环境。在此基础上，我们提出AgriMAE，这是一种参数高效的持续预训练基线方法，通过仅训练轻量适配器来适配在自然图像上预训练的掩码自编码器（masked autoencoder）。我们进一步探索语义特征重构作为替代预训练目标，并评估其在多个任务上的迁移效果。AgriMAE始终能提升下游任务性能，可达到甚至超过全微调的效果，同时仅使用至多9倍更少的可训练参数，表明AgriField-40K是农业视觉领域持续预训练的实用资源。项目页面：this https URL
英文摘要
Field-based agricultural computer vision is important for precision agriculture, yet it largely depends on expensive annotations and costly adaptation of large pretrained models. We introduce AgriField-40K, a field-centric dataset curated from 17 public resources and covering diverse crops, weeds, and field conditions. Building on this, we present AgriMAE, a parameter-efficient continual pretraining baseline that adapts a masked autoencoder pretrained on natural images by training only lightweight adapters. We further explore semantic feature reconstruction as an alternative pretraining objective and evaluate transfer across multiple tasks. AgriMAE consistently improves downstream performance and can match or even outperform full fine-tuning while using up to $9\times$ fewer trainable parameters, showing that AgriField-40K is a practical resource for continual pretraining in agricultural vision. Project page: https://dtu-pas.github.io/agrifield40k/
CommentsAccepted at the Computer Vision in Plant Phenotyping and Agriculture (CVPPA) Workshop at the European Conference on Computer Vision (ECCV) 2026
