Bridging Domain Gaps in Agricultural Image Analysis: A Comprehensive Review From Shallow Adaptation to Deep Learning

• URL: http://arxiv.org/abs/2506.05972v2
• Date: Fri, 20 Jun 2025 09:02:12 GMT
• Title: Bridging Domain Gaps in Agricultural Image Analysis: A Comprehensive Review From Shallow Adaptation to Deep Learning
• Authors: Xing Hu, Siyuan Chen, Xuming Huang, Qianqian Duan, LingKun Luo, Ruijiao Li, Huiliang Shang, Linhua Jiang, Jianping Yang, Hamid Reza Karimi, Dawei Zhang,
• Abstract summary: This paper investigates how Domain Adaptation techniques can address challenges by improving cross-domain transferability in agricultural image analysis.<n>The review systematically summarizes recent advances in DA for agricultural imagery, focusing on applications such as crop health monitoring, pest detection, and fruit recognition.
• Score: 17.455138644418618
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With the growing application of computer vision in agriculture, image analysis has become essential for tasks such as crop health monitoring and pest detection. However, significant domain shifts caused by environmental variations, different crop types, and diverse data acquisition methods hinder model generalization across regions, seasons, and complex agricultural settings. This paper investigates how Domain Adaptation (DA) techniques can address these challenges by improving cross-domain transferability in agricultural image analysis. Given the limited availability of labeled data, weak model adaptability, and dynamic field conditions, DA has emerged as a promising solution. The review systematically summarizes recent advances in DA for agricultural imagery, focusing on applications such as crop health monitoring, pest detection, and fruit recognition, where DA methods have enhanced performance across diverse domains. DA approaches are categorized into shallow and deep learning methods, including supervised, semi-supervised, and unsupervised strategies, with particular attention to adversarial learning-based techniques that have demonstrated strong potential in complex scenarios. In addition, the paper reviews key public agricultural image datasets, evaluating their strengths and limitations in DA research. Overall, this work offers a comprehensive framework and critical insights to guide future research and development of domain adaptation in agricultural vision tasks.

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