Detecting spikes of wheat plants using neural networks with Laws texture energy.
Detecting spikes of wheat plants using neural networks with Laws texture energy.
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DOI：
10.1186/s13007-017-0231-1
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发表时间：
2017
期刊：
影响因子：
5.1
通讯作者：
Miklavcic SJ
中科院分区：
文献类型：
--
作者：
Qiongyan L;Cai J;Berger B;Okamoto M;Miklavcic SJ
谷类作物的穗部是结实的器官，其物理特性是衡量粮食产量的指标。因此，从小麦等谷类植物的2D图像中检测和表征穗状花序的能力，提供了有关分蘖数量和产量潜力的重要信息。提出了一种新的小麦穗部检测方法，该方法首先采用改进的颜色指数法进行植株分割，然后利用LawS纹理能量进行基于神经网络的穗部检测。通过使用面积和高度阈值去除噪声，进一步改进了尖峰检测步骤。评价结果表明，对棘波的识别准确率达到80%以上。在所提出的方法中，我们还测量了不同实验条件下的单个穗的面积以及单个植物的所有穗的面积。文中还讨论了最终平均粮食产量与穗部面积的关系。我们的高精度产量性状表型方法用于穗数计数和穗面积估计，不仅对粮食产量估计有用和可靠，而且对于检测和量化由遗传或环境差异引起的细微表型变异也是有用和可靠的。
The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential. We have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The spike detection step was further improved by removing noise using an area and height threshold. The evaluation results showed an accuracy of over 80% in identification of spikes. In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The correlation between the final average grain yield and spike area is also discussed in this paper. Our highly accurate yield trait phenotyping method for spike number counting and spike area estimation, is useful and reliable not only for grain yield estimation but also for detecting and quantifying subtle phenotypic variations arising from genetic or environmental differences.