论文标题
当您只看一次时，基于图像的高粱头在计数
Image-Based Sorghum Head Counting When You Only Look Once
论文作者
论文摘要
数字农业的现代趋势已经转向人工智能，以进行农作物质量评估和产量估计。在这项工作中，我们记录了如何使用参数调整的单弹对象检测算法来识别和计算空中无人机图像中的高粱头。我们的方法涉及一项新颖的探索性分析，该分析确定了高粱图像的关键结构元素，并激发了参数调节的锚盒的选择，这些锚盒对性能产生了重大贡献。这些见解导致了一个深度学习模型的发展，该模型胜过基线模型，并达到了样本外平均平均精度为0.95。
Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation. In this work, we document how a parameter tuned single-shot object detection algorithm can be used to identify and count sorghum head from aerial drone images. Our approach involves a novel exploratory analysis that identified key structural elements of the sorghum images and motivated the selection of parameter-tuned anchor boxes that contributed significantly to performance. These insights led to the development of a deep learning model that outperformed the baseline model and achieved an out-of-sample mean average precision of 0.95.
