Optimum plant stand density and uniformity is vital in order to maximize corn (Zea mays L.) yield potential. Assessment of stand density can occur shortly after seedlings begin to emerge, allowing for timely replant decisions. The conventional methods for evaluating an early plant stand rely on manual measurement and visual observation, which are time consuming, subjective because of the small sampling areas used, and unable to capture field-scale spatial variability. This study aimed to evaluate the feasibility of an unmanned aerial vehicle (UAV)-based imaging system for estimating early corn stand count in three cropping systems (CS) with different tillage and crop rotation practices. A UAV equipped with an on-board RGB camera was used to collect imagery of corn seedlings (~14 days after planting) of CS, i.e., minimum-till corn-soybean rotation (MTCS), no-till corn-soybean rotation (NTCS), and no-till corn-corn rotation with cover crop implementation (NTCC). An image processing workflow based on a deep learning (DL) model, U-Net, was developed for plant segmentation and stand count estimation. Results showed that the DL model performed best in segmenting seedlings in MTCS, followed by NTCS and NTCC. Similarly, accuracy for stand count estimation was highest in MTCS (R² = 0.95), followed by NTCS (0.94) and NTCC (0.92). Differences by CS were related to amount and distribution of soil surface residue cover, with increasing residue generally reducing the performance of the proposed method in stand count estimation. Thus, the feasibility of using UAV imagery and DL modeling for estimating early corn stand count is qualified influenced by soil and crop management practices.

为了使玉米最大化（玉米（Zea mays），最佳的植物密度和均匀度至关重要。L.）产生潜力。可以在幼苗开始出现后不久评估林分密度，以便及时做出补种决定。评估早期植物立场的常规方法依赖于手动测量和视觉观察，这非常耗时，主观原因，因为使用的采样面积较小，并且无法捕获田间规模的空间变异性。这项研究旨在评估基于无人机的成像系统在不同耕作和轮作方式下的三种作物系统（CS）中估算早期玉米林分数量的可行性。使用装有机载RGB摄像头的无人机收集CS的玉米幼苗（种植后约14天）的图像，即最小耕种玉米-大豆轮作（MTCS），免耕玉米-大豆轮作（NTCS） ），以及采用覆盖作物实施（NTCC）的免耕玉米-玉米轮作。开发了基于深度学习（DL）模型U-Net的图像处理工作流程，用于植物分割和林分数量估计。结果表明，DL模型在MTCS中分割种子方面表现最好，其次是NTCS和NTCC。同样，MTCS中林分数量估计的准确性最高（R ²  = 0.95），然后是NTCS（0.94）和NTCC（0.92）。CS的差异与土壤表层残留物覆盖量和分布有关，增加残留物通常会降低该方法在林分数量估算中的性能。因此，使用UAV影像和DL模型估算早期玉米林分的可行性受到土壤和作物管理实践的影响。