Gao Jiaxin, Tan Feng, Cui Jiapeng, Hou Zhaolong
College of Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.
Front Plant Sci. 2025 May 23;16:1569229. doi: 10.3389/fpls.2025.1569229. eCollection 2025.
Missing seedlings is a common issue in field maize planting, arising from limitations in sowing machinery and seed germination rates. This phenomenon directly impacts maize yields owing to the poor effect of unmanned aerial vehicle (UAV) remote sensing images based on seedling leakage detection in fields. Therefore, this study proposed a method for detecting missing seedling in fields based on UAV remote sensing to quickly and accurately detect missing seedling and facilitate subsequent crop management decisions.
The method calculates the rated inter-seedling distance in UAV-captured images of maize fields using a combination of image processing techniques, including background segmentation, stalk center region detection, linear fitting of plant rows, and average plant distance calculation. Based on these calculations, an improved Maize-YOLOv8n model was employed to detect actual seedling emergence.
The experimental results demonstrate that the new model achieved superior performance on a self-constructed dataset, with a mean average precision (mAP) of 97.4%, precision (P) of 94.3%, recall (R) of 93.1%, and an F1 score of 93.7%. The model was lightweight, comprising only 1.19 million parameters and requiring 20.2 floating-point operations per second (FLOPs). The inference time was 12.8 ms, satisfying real-time detection requirements. Performance evaluations across various conditions, including different leaf stages, light intensities, and weed interference levels, further indicated the robustness of the model. In addition, a linear regression equation was developed to predict the total number of missing seedlings, with model performance evaluated using the root mean squared error (RMSE) and mean absolute error (MAE) metrics.
The results confirm the ability of the model to accurately detect maize seedling gaps. This study can evaluate the quality of seeding operations and provide accurate information on the number of missing seedlings for timely replacement work in areas with high rates of missing seedlings. This study advances precision agriculture by enhancing the efficiency and accuracy of maize planting management.
缺苗是田间玉米种植中的常见问题，这是由播种机械和种子发芽率的限制导致的。由于基于田间缺苗检测的无人机（UAV）遥感图像效果不佳，这种现象直接影响玉米产量。因此，本研究提出了一种基于无人机遥感的田间缺苗检测方法，以快速、准确地检测缺苗情况，并为后续作物管理决策提供便利。
该方法结合图像处理技术，包括背景分割、茎秆中心区域检测、植株行的线性拟合和平均株距计算，来计算无人机拍摄的玉米田图像中的额定苗间距。基于这些计算，采用改进的Maize-YOLOv8n模型来检测实际出苗情况。
实验结果表明，新模型在自建数据集上表现优异，平均精度均值（mAP）为97.4%，精确率（P）为94.3%，召回率（R）为93.1%，F1分数为93.7%。该模型轻量级，仅包含119万个参数，每秒需要20.2次浮点运算（FLOPs）。推理时间为12.8毫秒，满足实时检测要求。在包括不同叶期、光照强度和杂草干扰水平等各种条件下的性能评估进一步表明了该模型的稳健性。此外，还建立了一个线性回归方程来预测缺苗总数，并使用均方根误差（RMSE）和平均绝对误差（MAE）指标评估模型性能。
结果证实了该模型能够准确检测玉米苗间隙。本研究可以评估播种作业质量，并为缺苗率高的地区及时补种工作提供准确的缺苗数量信息。本研究通过提高玉米种植管理的效率和准确性，推动了精准农业的发展。
