玉米幼苗检测数据集（MSDD）：一个用于玉米幼苗检测及与YOLOv9、YOLO11、YOLOv12和Faster-RCNN基准测试的精选高分辨率RGB数据集
Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN
AI总结：
本研究推出用于玉米幼苗株数统计的高质量航空图像数据集MSDD，涵盖多类植株与多样田间条件，通过基准测试明确不同模型的检测性能差异，为精准农业相关模型开发提供支撑。
摘要：
精准的玉米幼苗检测对精准农业至关重要，但精选数据集仍然稀缺。我们推出了MSDD，这是一个用于玉米幼苗株数统计的高质量航空图像数据集，可应用于作物早期监测、产量预测和田间管理。株数统计可确定发芽植株的数量，为补种或调整投入等及时决策提供指导。传统方法劳动强度大且易出错，而计算机视觉可实现高效、准确的检测。MSDD包含三类——单株、双株和三株植株，涵盖了不同的生长阶段、种植模式、土壤类型、光照条件、拍摄角度和种植密度，确保了在实际应用中的鲁棒性。基准测试表明，在V4-V6生长阶段和正射视角下，检测结果最为可靠。在测试的模型中，YOLO11速度最快，而YOLOv9对单株植株的检测准确率最高。单株检测的精确率最高可达0.984，召回率最高可达0.873，但由于双株和三株植株较为罕见且外观不规则（通常由种植误差导致），检测它们仍然存在困难。类别不平衡进一步降低了多株检测的准确率。尽管存在这些挑战，YOLO11仍保持了高效的推理速度，每张图像推理耗时35毫秒，额外保存输出结果需120毫秒。MSDD为开发提升株数统计能力、优化资源配置并支持实时决策的模型奠定了坚实基础。该数据集是迈向农业监测自动化和推进精准农业的重要一步。
英文摘要：
Accurate maize seedling detection is crucial for precision agriculture, yet curated datasets remain scarce. We introduce MSDD, a high-quality aerial image dataset for maize seedling stand counting, with applications in early-season crop monitoring, yield prediction, and in-field management. Stand counting determines how many plants germinated, guiding timely decisions such as replanting or adjusting inputs. Traditional methods are labor-intensive and error-prone, while computer vision enables efficient, accurate detection. MSDD contains three classes-single, double, and triple plants-capturing diverse growth stages, planting setups, soil types, lighting conditions, camera angles, and densities, ensuring robustness for real-world use. Benchmarking shows detection is most reliable during V4-V6 stages and under nadir views. Among tested models, YOLO11 is fastest, while YOLOv9 yields the highest accuracy for single plants. Single plant detection achieves precision up to 0.984 and recall up to 0.873, but detecting doubles and triples remains difficult due to rarity and irregular appearance, often from planting errors. Class imbalance further reduces accuracy in multi-plant detection. Despite these challenges, YOLO11 maintains efficient inference at 35 ms per image, with an additional 120 ms for saving outputs. MSDD establishes a strong foundation for developing models that enhance stand counting, optimize resource allocation, and support real-time decision-making. This dataset marks a step toward automating agricultural monitoring and advancing precision agriculture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
