Accurately identifying the quantity of maize seedlings is useful in improving maize varieties with high seedling emergence rates in a breeding program. The traditional method is to calculate the number of crops manually, which is labor-intensive and time-consuming. Recently, observation methods utilizing a UAV have been widely employed to monitor crop growth due to their low cost, intuitive nature and ability to collect data without contacting the crop. However, most investigations have lacked a systematic strategy for seedling identification. Additionally, estimating the quantity of maize seedlings is challenging due to the complexity of field crop growth environments. The purpose of this research was to rapidly and automatically count maize seedlings. Three models for estimating the quantity of maize seedlings in the field were developed: corner detection model (C), linear regression model (L) and deep learning model (D). The robustness of these maize seedling counting models was validated using RGB images taken at various dates and locations. The maize seedling recognition rate of the three models were 99.78% (C), 99.9% (L) and 98.45% (D) respectively. The L model can be well adapted to different data to identify the number of maize seedlings. The results indicated that the high-throughput and fast method of calculating the number of maize seedlings is a useful tool for maize phenotyping.

准确识别玉米幼苗的数量有助于在育种计划中改良具有高出苗率的玉米品种。传统的方法是人工计算作物的数量，既费力又费时。最近，利用无人机的观察方法已被广泛用于监测作物生长，因为它们成本低、直观且能够在不接触作物的情况下收集数据。然而，大多数调查都缺乏系统的幼苗鉴定策略。此外，由于大田作物生长环境的复杂性，估计玉米幼苗的数量具有挑战性。本研究的目的是快速、自动地计数玉米幼苗。开发了三种估算田间玉米幼苗数量的模型：角点检测模型（C）、线性回归模型（L）和深度学习模型（D）。使用在不同日期和地点拍摄的 RGB 图像验证了这些玉米幼苗计数模型的稳健性。三种模型的玉米幼苗识别率分别为99.78%（C）、99.9%（L）和98.45%（D）。L模型可以很好地适应不同的数据来识别玉米苗的数量。结果表明，高通量、快速计算玉米幼苗数量的方法是玉米表型分析的有用工具。分别为 45% (D)。L模型可以很好地适应不同的数据来识别玉米苗的数量。结果表明，高通量、快速计算玉米幼苗数量的方法是玉米表型分析的有用工具。分别为 45% (D)。L模型可以很好地适应不同的数据来识别玉米苗的数量。结果表明，高通量、快速计算玉米幼苗数量的方法是玉米表型分析的有用工具。