基于无人机RGB影像估测田间小麦穗数

doi:10.13304/j.nykjdb.2021.0335

摘要/Abstract

摘要：

单位面积穗数是小麦产量构成的重要因素，利用图像信息处理技术快速、准确地估测田间小麦穗数，可以为小麦长势监测和产量估测提供直接依据。利用无人机路径规划和控制系统（fragmentation monitoring and analysis with aerial photography， FragMAP）获取标准统一、高分辨率的田间小麦RGB航拍影像，通过高效的目标检测手段（YOLOv3）获得训练模型并自动识别麦穗，通过分析该方法（FY方法）与传统方法测定麦穗数量的关系来构建单位面积麦穗估测模型。结果表明，FY方法的样本获取效率和观测面积显著高于传统方法（P < 0.001）；YOLOv3训练模型识别麦穗的准确率随着训练样本数量和迭代次数增加而增加，500个训练样本迭代6 250次，获得模型识别麦穗的准确率超过90%；FY方法和与传统方法测定的田间小麦穗数量呈显著的线性相关关系，据此构建估测田间小麦穗数的模型为：y=0.816x-14.863（R²= 0.790，P<0.001）。上述结果表明，结合标准统一、高分辨率的无人机航拍影像和深度学习方法估测田间小麦穗数精度高、实时性强，可为小麦长势监测和产量估测提供重要的数据和技术支撑。

关键词: 航拍, FragMAP, 深度学习, YOLOv3, 麦穗检测

Abstract:

Ears number per unit area is the key element of wheat yield， so estimating ears number in field quickly and accurately based on aerial photographs and information extraction could provide the direct support to wheat growth monitoring and production estimates. This study acquired the unified standard and high resolution RGB aerial photographs using unmanned aerial vehicle （UAV） controlled by fragmentation monitoring and analysis with aerial photography （FragMAP）， then obtained the training model by quick and efficient target detection method （YOLOv3） and realized identifying wheat ears automatically， and finally established the estimation model of wheat ears number based on the relationships of the wheat ears number measured by proposed method （FY） and traditional methods. The results showed that the sampling efficiency and monitor area of FY were significantly higher than that of traditional method； the mean accuracy rate of identification based on YOLOv3 model was above 90%； the wheat ears numbers measured by FY and traditional method were significant linear correlation， and the estimate model was established as y=0.816x-14.863 （R²=0.790， P<0.001）. The results showed that the wheat ears estimation based on the unified standard and higher resolution images collected by UAV and deep learning was accurate， and could effectively monitor wheat growth and predicting the wheat yield.

Key words: aerial photograph, FragMAP, deep learning, YOLOv3, wheat ears detection

中图分类号:

S127

高姻燕, 孙义, 李葆春. 基于无人机RGB影像估测田间小麦穗数[J]. 中国农业科技导报, 2022, 24(3): 103-110.

Yinyan GAO, Yi SUN, Baochun LI. Estimating of Wheat Ears Number in Field Based on RGB Images Using Unmanned Aerial Vehicle[J]. Journal of Agricultural Science and Technology, 2022, 24(3): 103-110.

图/表 6

图1 研究区域及采样点

Fig.1 Study area and filed sampling plots

图2 无人机航拍获取麦穗信息

Fig.2 Collection of wheat ears information by UAV

图3 FY方法与传统方法野外采集样本效率比较注：***表示处理在P < 0.001水平差异显著。

Fig.3 Field sampling efficiency of FY and traditional methodsNote：*** indicates significant difference between methods at P<0.001 level.

图4 3种训练处理方式获得最优模型识别小麦麦穗的案例

Fig.4 Examples of the recognition by the 3 trained models

图5 训练模型识别麦穗准确率与训练样本数量和迭代次数的关系注：图中不同小写字母表示P<0.05水平差异显著。

Fig.5 Relationship between recognition accuracy of training models and sample number and iterationsNote： Different letters indicate significant differences at P<0.05 level.

图6 基于地面实测与FY方法获取数据构建田间小麦穗数预测模型注：图中虚线为1∶1线。

Fig.6 Prediction model based on the data collected using traditional and FY methodNote： Dashed line indicates line 1∶1.

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