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一种计算小麦第一至三叶期麦苗数量的方法。

A method to calculate the number of wheat seedlings in the 1st to the 3rd leaf growth stages.

作者信息

Liu Tao, Yang Tianle, Li Chunyan, Li Rui, Wu Wei, Zhong Xiaochun, Sun Chengming, Guo Wenshan

机构信息

1Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, 225009 China.

2Key Laboratory of Agro-information Services Technology, Ministry of Agriculture, Beijing, 100081 China.

出版信息

Plant Methods. 2018 Nov 16;14:101. doi: 10.1186/s13007-018-0369-5. eCollection 2018.

DOI:10.1186/s13007-018-0369-5
PMID:30473722
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6238276/
Abstract

BACKGROUND

The number of cultivated wheat seedlings per unit area allows calculation of plant density. Wheat seedling density provides emergence data and this is useful for improving crop management. The number of wheat seedlings is typically determined by visual counts but this is time-consuming and laborious.

RESULTS

We obtained field digital images of 1st to 3rd leaf stage wheat seedlings. The seedlings were extracted using an image analysis technique that calculated the coverage degree of the seedlings and the number of angular points of overlapping leaves. The wheat seedling quantity estimation model was constructed using multivariate regression analysis. The model parameters included coverage degree, number of angular points, variety coefficient, and leaf age. Introduction of the number of angular points increased the accuracy of the single coverage degree model. The R value was consistently > 0.95 when the model was applied to different varieties, indicating that the model was adaptable for different varieties. As the leaf stage or density increased, the accuracy of the model declined, but the minimum R remained > 0.87, indicating good adaptability of the model to seedlings with different leaf ages and densities.

CONCLUSIONS

This method is an effective means for counting wheat seedlings in the 1st to the 3rd leaf stages.

摘要

背景

单位面积内栽培小麦幼苗的数量可用于计算种植密度。小麦幼苗密度可提供出苗数据,这对改善作物管理很有用。小麦幼苗数量通常通过目视计数来确定,但这既耗时又费力。

结果

我们获取了处于第一至三叶期小麦幼苗的田间数字图像。利用一种图像分析技术提取幼苗,该技术可计算幼苗的覆盖度和重叠叶片的角点数量。使用多元回归分析构建了小麦幼苗数量估计模型。模型参数包括覆盖度、角点数量、品种系数和叶龄。角点数量的引入提高了单一覆盖度模型的准确性。当该模型应用于不同品种时,R值始终大于0.95,表明该模型适用于不同品种。随着叶龄或密度增加,模型的准确性下降,但最小R值仍大于0.87,表明该模型对不同叶龄和密度的幼苗具有良好的适应性。

结论

该方法是对第一至三叶期小麦幼苗进行计数的有效手段。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/ae4cb229c8e9/13007_2018_369_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/85c80a24c11c/13007_2018_369_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/02b167848ba8/13007_2018_369_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/7ad2cbf88565/13007_2018_369_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/84000862ada2/13007_2018_369_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/f0e4739b320e/13007_2018_369_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/f32e9b7f32ab/13007_2018_369_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/ae4cb229c8e9/13007_2018_369_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/85c80a24c11c/13007_2018_369_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/4c96de139e4c/13007_2018_369_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/7815618bf71e/13007_2018_369_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/0767de22bd16/13007_2018_369_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/02b167848ba8/13007_2018_369_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/7ad2cbf88565/13007_2018_369_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/84000862ada2/13007_2018_369_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/f0e4739b320e/13007_2018_369_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/20fd8db535be/13007_2018_369_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/3aa2bf4c0e18/13007_2018_369_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/f32e9b7f32ab/13007_2018_369_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf28/6238276/ae4cb229c8e9/13007_2018_369_Fig12_HTML.jpg

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