| 基于堆叠沙漏网络的单分蘖水稻植株骨架提取 |
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| 引用本文： | 王敏娟,刘小丫,马啸霄,常天根,宋青峰.基于堆叠沙漏网络的单分蘖水稻植株骨架提取[J].农业工程学报,2021,37(24):149-157. |
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| 作者姓名： | 王敏娟 刘小丫 马啸霄 常天根 宋青峰 |
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| 作者单位： | 1. 中国农业大学现代精细农业系统集成研究教育部重点实验室，北京，100083;2. 中国科学院分子植物科学卓越创新中心，植物分子遗传国家重点实验室，上海 200032 |
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| 基金项目： | 国家自然科学基金项目（31970378），山东省自然科学基金面上项目（ZR2021MC021） |
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| 摘 要： | 针对水稻栽培和遗传育种研究中单分蘖性状高通量无损提取的实际需求，该研究提出了一种基于沙漏网络模型的单分蘖水稻关键点预测和骨架提取方法。首先，对原始图像进行批量裁剪、gamma校正和锐化卷积等预处理，获取单色背景下的水稻单分蘖图像数据集；设计水稻单分蘖各器官关键点数据标注策略，构建监督数据集。然后，构建堆叠沙漏网络架构实现叶片数固定和不固定的水稻关键点检测，引入沙漏结构整合图像的多尺度特征，结合中间监督机制整合不同沙漏模块信息。叶片数一致的情况，模型预测准确率最高可达96.48%；叶片数不一致的情况，预测准确率达到82.09%。最后，根据预测关键点及其对应的语义信息连接形成植株骨架，选取茎秆长、叶片长、穗长、叶片-茎秆夹角和茎节点位置5个表型参数对生成骨架模型的实际意义进行评估，其均方根误差依次为5.82 cm、3.09 cm、1.71 cm、3.22°和2.04 cm，证明了该方法能较好地识别水稻单分蘖关键点，为水稻骨架提取提供了一种新思路，有助于加快水稻育种速度。
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| 关 键 词： | 水稻 神经网络 表型参数 关键点检测 堆叠沙漏网络 植株骨架提取 |
| 收稿时间： | 2021/7/17 0:00:00 |
| 修稿时间： | 2021/11/25 0:00:00 |
Skeleton extraction method of single tillers rice based on stacked hourglass network |
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| Wang Minjuan,Liu Xiaoy,Ma Xiaoxiao,Chang Tiangen,Song Qingfeng.Skeleton extraction method of single tillers rice based on stacked hourglass network[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(24):149-157. |
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| Authors: | Wang Minjuan Liu Xiaoy Ma Xiaoxiao Chang Tiangen Song Qingfeng |
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| Institution: | 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing 100083, China;;2. National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China |
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| Abstract: | Abstract: Rice has been widely planted in tropical Asia, where South China is the main rice-producing area. Rice production has accounted for about 50% of the total grain in China. The phenotype of rice can greatly contribute to clarifying the physical, physiological, and biochemical characteristics, thereby effectively promoting the rice varieties and yield. However, the morphology of a single tiller can usually be characterized by the contact destructive measurement in traditional rice cultivation, indicating a cost-consuming, labor-intensive and subjective task. In this study, a rice key point detection was proposed to rapidly extract the parameters of rice phenotype using a Stacked Hourglass Network (SHN) of computer vision. Firstly, a total of 1 080 RGB images of rice were collected. Some operations were then selected to preprocess the raw images of single tiller rice with the monochromatic background, including clipping, gamma correction, and Laplacian sharpening convolution. The data was also labeled to form the supervised dataset, according to the preset key point number of each rice component. Secondly, an SHN was adapted to detect the rice key points with the fixed and unfixed leaf number. A block of hourglass was then formed to combine the sampling, the maximum pooling, and the residual together. After that, intermediate supervision was adapted to generate the prediction from all connected hourglass blocks. As such, a multi-scale fusion was contributed to a better prediction from different scales. Two ways were also proposed to add the blank data for the unfixed leaf number. Finally, an optimal parametric combination was achieved in the experiments. The results showed that the accuracy rates reached 85.84% and 82.09% in the training and test set, respectively, when the epoch was equaled to 119 in the key point regression task with unfixed leaf numbers. The highest prediction accuracy reached 96.48% in the case of the fixed number of leaves. Correspondingly, the predicted key points of single tiller rice were connected to form a plant skeleton, according to the semantic information, indicating a better performance for the actual growth of plants. Furthermore, five parameters were selected to verify the model, including the stem length, leaf length, ear length, leaf stem angle, and stem node position. It was found that the Root Mean Square Errors (RMSE) were 5.82 cm, 3.09 cm, 1.71 cm, 3.22°, and 2.04 cm, respectively, indicating a better matching for the actual growth of rice. More importantly, the error of phenotypic parameters and some damage to plants were significantly reduced, compared with the previous manual measurement. The finding can also provide a new idea to efficiently extract the plant skeleton for the phenotypic parameters in rice production. |
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| Keywords: | rice neural networks phenotypic parameters key point detection Stacked Hourglass Networks plant skeleton extraction |
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