Abstract
Efficient crop detection via Unmanned Aerial Vehicles is critical for scaling precision agriculture, yet it remains challenging due to the small scale of targets and environmental variability. This paper addresses the detection of rice seedlings in paddy fields by leveraging a Faster R-CNN architecture initialized via transfer learning. To overcome the specific difficulties of detecting minute objects in high-resolution aerial imagery, we curate a significant UAV dataset for training and rigorously evaluate the model's generalization capabilities. Specifically, we validate performance across three distinct test sets acquired at different temporal intervals, thereby assessing robustness against varying imaging conditions. Our empirical results demonstrate that transfer learning not only facilitates the rapid convergence of object detection models in agricultural contexts but also yields consistent performance despite domain shifts in image acquisition.
Abstract (translated)
通过无人驾驶飞行器（UAV）进行高效的作物检测对于扩大精准农业的应用至关重要，但由于目标规模小和环境变化大等原因，这一任务仍然具有挑战性。本文利用Faster R-CNN架构并通过迁移学习进行初始化，解决了在水田中检测水稻幼苗的问题。为了克服在高分辨率的航空图像中检测微小对象的具体困难，我们整理了一个重要的UAV数据集用于训练，并严格评估模型的泛化能力。具体来说，我们在三个不同时间间隔获取的不同测试集中验证了性能，从而评估了模型对变化成像条件下的鲁棒性。我们的实证结果表明，迁移学习不仅有助于在农业环境中快速收敛物体检测模型，而且即使图像采集领域发生变化也能保持一致的性能。
URL
https://arxiv.org/abs/2512.11360
