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Maize emergence rate and leaf emergence speed estimation via image detection under field rail-based phenotyping platform

文献类型： 外文期刊

作者： Zhuang, Lvhan 1 ; Wang, Chuanyu 2 ; Hao, Haoyuan 1 ; Li, Jinhui 1 ; Xu, Longqin 1 ; Liu, Shuangyin 1 ; Guo, Xinyu 2 ;

作者机构： 1.Zhongkai Univ Agr & Engn, Guangzhou 510225, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

4.Zhongkai Univ Agr & Engn, Guangzhou Key Lab Agr Prod Qual, Safety Traceabil Informat Technol, Guangzhou 510225, Peoples R China

5.Zhongkai Univ Agr & Engn, Coll Informat Sci & Technol, Guangzhou 510225, Peoples R China

关键词： Field rail-based phenotyping platform; Emergence rate; Leaf emergence speed; Faster R-CNN; Mask R-CNN

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 220 卷

页码：

收录情况： SCI

摘要： Accurate and efficient acquisition of maize emergence rate and leaf emergence speed in the field is essential for detecting seed quality, evaluating crop field management plans, and yield assessment. This study constructs a system solution to obtain the maize seedling emergence rate and leaf emergence speed based on the field railbased phenotyping platform and convolutional neural network. Firstly, we use the field rail-based phenotyping platform to collect a high-temporal sequence visible light images of maize plant during the seedling stage. In the first stage, an improved Faster R-CNN is used to detect maize seedlings in the plot images, and the plant ROI area is cropped as the input for the second stage network. In the second stage, the best performing ResNeSt network out of four backbone networks is chosen, using the Mask R-CNN model to segment the leaves of the input plant image, which is then used to calculate the number of leaves. We propose a quantification index for leaf emergence speed based on a weighted average combination of leaf numbers. Using the method described in this paper, we analyzed the plant images from 52 inbred lines plots of over seven consecutive days. The experimental results show that when the Intersection Over Union (IOU) is 0.50, the bbox_mAP of the maize seedling detection model is 0.969, with an accuracy rate of 99.53%. Compared with manual counting, the calculated R2 is 0.997 and RMSE is 43.382. The segm_mAP of the plant leaf segmentation model is 0.942. The differences in emergence rate and leaf emergence speed across 52 inbred lines were compared, providing new phenotyping reference indices for further exploring the genotypic differences affecting seed emergence and leafing.

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