Kavli Affiliate: Linhua Jiang
| First 5 Authors: Xing Hu, Siyuan Chen, Xuming Huang, Qianqian Duan, LingKun Luo
| Summary:
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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