Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis.
Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis.
复制标题
使用 k-NN 分析芒草不同发芽表型的图像并检测单粒种子的发芽情况。
DOI：
10.1186/s13007-018-0272-0
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发表时间：
2018
期刊：
影响因子：
5.1
通讯作者：
Robson P
中科院分区：
文献类型：
--
作者：
Awty-Carroll D;Clifton-Brown J;Robson P
芒草是第二代生物能源作物。它主要是根茎繁殖;然而，越来越多地使用种子导致更需要调查发芽。芒属植物种子小，发芽通常很差，并且在没有灭菌的情况下进行;因此，应用于发芽检测的自动化方法必须能够科普例如小物体的阈值化、低发芽频率以及霉菌的存在或不存在。使用k-NN的机器学习改进了芒属种子中遇到的不同表型的评分。基于k-NN的算法是有效的种子图像的发芽评分相比，人类的分数相同的图像。k-NN结果的真实度为0.69-0.7，使用ROC曲线下面积测量。当在种子的优化图像子集上测试k-NN分类器时，实现了0.89的ROC曲线下面积。这种方法比一种已确立的技术好。对于包括霉菌和破碎种子的非理想种子图像，k-NN分类器与人类评估不太一致。训练分类器的最准确的发芽评估很难确定，但k-NN分类器提供了这一重要特征的公正一致的测量。它比现有的人类评分方法更具重现性，并被证明可以为人类评分提供高度的真实性。
Miscanthus is a leading second generation bio-energy crop. It is mostly rhizome propagated; however, the increasing use of seed is resulting in a greater need to investigate germination. Miscanthus seed are small, germination is often poor and carried out without sterilisation; therefore, automated methods applied to germination detection must be able to cope with, for example, thresholding of small objects, low germination frequency and the presence or absence of mould. Machine learning using k-NN improved the scoring of different phenotypes encountered in Miscanthus seed. The k-NN-based algorithm was effective in scoring the germination of seed images when compared with human scores of the same images. The trueness of the k-NN result was 0.69–0.7, as measured using the area under a ROC curve. When the k-NN classifier was tested on an optimised image subset of seed an area under the ROC curve of 0.89 was achieved. The method compared favourably to an established technique. With non-ideal seed images that included mould and broken seed the k-NN classifier was less consistent with human assessments. The most accurate assessment of germination with which to train classifiers is difficult to determine but the k-NN classifier provided an impartial consistent measurement of this important trait. It was more reproducible than the existing human scoring methods and was demonstrated to give a high degree of trueness to the human score.
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影响因子：
7.2
作者：
Joosen, Ronny V. L.;Kodde, Jan;Hilhorst, Henk W. M.
通讯作者：
Hilhorst, Henk W. M.
影响因子：
5.6
作者：
Hastings A;Mos M;Yesufu JA;McCalmont J;Schwarz K;Shafei R;Ashman C;Nunn C;Schuele H;Cosentino S;Scalici G;Scordia D;Wagner M;Clifton-Brown J
通讯作者：
Clifton-Brown J
影响因子：
2
作者：
Achigan-Dako, Enoch G.;Avohou, Edgar S.;Blattner, Frank R.
通讯作者：
Blattner, Frank R.
影响因子：
1.4
作者：
Ducournau, S;Feutry, A;Wagner, MH
通讯作者：
Wagner, MH
影响因子：
2.3
作者：
Christian, Erik J.;Goggi, A. Susana;Moore, Kenneth J.
通讯作者：
Moore, Kenneth J.