Modern agricultural operations increasingly rely on integrated monitoring systems that combine multiple data sources for farm optimization. Aerial drone-based animal health monitoring serves as a key component but faces limited data availability, compounded by scene-specific issues such as small, occluded, or partially visible animals. Transfer learning approaches often fail to address this limitation due to the unavailability of large datasets that reflect specific farm conditions, including variations in animal breeds, environments, and behaviors. Therefore, there is a need for developing a problem-specific, animal-focused data augmentation strategy tailored to these unique challenges. To address this gap, we propose an object-focused data augmentation framework designed explicitly for animal health monitoring in constrained data settings. Our approach segments animals from backgrounds and augments them through transformations and diffusion-based synthesis to create realistic, diverse scenes that enhance animal detection and monitoring performance. Our initial experiments demonstrate that our augmented dataset yields superior performance compared to our baseline models on the animal detection task. By generating domain-specific data, our method empowers real-time animal health monitoring solutions even in data-scarce scenarios, bridging the gap between limited data and practical applicability.

翻译：现代农业作业日益依赖整合多数据源的监测系统以实现农场优化。基于无人机航拍的动物健康监测作为关键组成部分，却面临数据可用性有限的挑战，且因场景特异性问题（如动物体型小、被遮挡或部分可见）而加剧。由于缺乏反映特定农场条件（包括动物品种、环境及行为差异）的大规模数据集，迁移学习方法往往难以克服这一局限。因此，有必要针对这些独特挑战开发问题特异性、以动物为核心的数据增强策略。为填补这一空白，我们提出了一种专为受限数据环境下动物健康监测设计的物体聚焦数据增强框架。该方法通过分割动物与背景，并借助变换和基于扩散的合成技术对动物进行增强，以创建真实且多样化的场景，从而提升动物检测与监测性能。初步实验表明，在动物检测任务上，使用本方法增强的数据集相较于基线模型展现出更优性能。通过生成领域特异性数据，本方法即使在数据稀缺场景下也能赋能实时动物健康监测解决方案，弥合有限数据与实际应用之间的鸿沟。