Accurate maize plant counting plays an essential role in prediction of leaf area index (LAI), aboveground biomass (AGB) and yield. Plant counting of maize inbred lines at early growth stage will result in counting bias caused by death and growth of small seedlings. Therefore, the estimation of LAI and AGB might be negatively affected by plant counting bias at early growth stage. In this study, morphologic discrimination model (MDM) and interpolation discriminant model (IDM) were proposed for plant counting of maize inbred lines at second to fourth (V2–V4) leaf and fourth to sixth (V4–V6) leaf stages with different uncrewed aerial vehicles (UAV) flight heights. Automatic optimum angle calculation of each row, location-based plant cluster segmentation and mosaic method were presented to improve the estimation accuracy of plant counting. Then, the impact of accurate plant counting was evaluated in LAI and AGB prediction at the two growth stages. The results indicated that germination rate difference of some inbred lines could reach up to 38% between V2–V4 and V4–V6 leaf stages. The proposed method accurately estimated the plant counting in the UAV images during V2–V4 leaf stage (R² = 0.98, RMSE = 7.7, rRMSE = 2.6%) and V4–V6 leaf stage (R² = 0.86, RMSE = 2.0, rRMSE = 5.5%). The estimated LAI and AGB with plant numbers calculated at V4–V6 leaf stage correlated better with the field measurements (R² = 0.85 and R² = 0.9, respectively) compared with those estimated at V2–V4 leaf stage (R² = 0.8 and R² = 0.86, respectively). This research indicates that better estimation of LAI and AGB in the field were obtained by accurate plant counting in the late growth stage using UAV images and provides valuable insight for more accurate prediction of yield and crop management and breeding.

准确的玉米植株计数在预测叶面积指数 (LAI)、地上生物量 (AGB) 和产量方面起着至关重要的作用。玉米自交系在生长早期的植株计数会导致小苗死亡和生长引起的计数偏差。因此，LAI 和 AGB 的估计可能会受到早期生长阶段植物计数偏差的负面影响。在这项研究中，提出了形态判别模型（MDM）和插值判别模型（IDM），用于玉米自交系在第二至第四（V2-V4）叶片和第四至第六（V4-V6）叶片阶段的植物计数飞行器 (UAV) 飞行高度。提出了自动计算每行最佳角度、基于位置的植物簇分割和镶嵌方法来提高植物计数的估计精度。然后，在两个生长阶段的 LAI 和 AGB 预测中评估了准确植物计数的影响。结果表明，一些自交系在V2-V4和V4-V6叶期之间的发芽率差异可达38%。所提出的方法准确估计了 V2-V4 叶期无人机图像中的植物计数（R ²  = 0.98, RMSE = 7.7, rRMSE = 2.6%) 和 V4-V6 叶期 ( R ²  = 0.86, RMSE = 2.0, rRMSE = 5.5%)。与 在 V2-V4叶期^（R 2 ⁼  0.8 和R ²  = 0.86，分别）。该研究表明，通过使用无人机图像在生长后期进行准确的植物计数，可以更好地估计田间的 LAI 和 AGB，并为更准确地预测产量和作物管理和育种提供有价值的见解。