Estimating maize seedling number with UAV RGB images and advanced image processing methods
文献类型： 外文期刊
作者： Liu, Shuaibing 1 ; Yin, Dameng 1 ; Feng, Haikuan 4 ; Li, Zhenhai 4 ; Xu, Xiaobin 1 ; Shi, Lei 1 ; Jin, Xiuliang 1 ;
作者机构： 1.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China
2.Chinese Acad Agr Sci, Natl Nanfan Res Inst Sanya, Sanya 572024, Peoples R China
3.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China
4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100089, Peoples R China
5.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
关键词： Maize seedlings; Unmanned aerial vehicle; Faster R-CNN; Linear regression model; Corner detection model
期刊名称：PRECISION AGRICULTURE （ 影响因子：5.767； 五年影响因子：5.875 ）
ISSN： 1385-2256
年卷期：
页码：
收录情况： SCI
摘要： 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.
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