基于深度学习的水稻表型特征提取和穗重预测研究
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华中农业大学

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Rice phenotypic traits extraction and prediction of panicle weight using deep learning
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    摘要:

    水稻产量与水稻穗数和穗子重量密切相关,精确预测水稻产量可以加快育种速度。为了研究水稻产量与植株表型特征之间的关系,本实验以盆栽水稻为研究对象,首先利用可见光图像结合图像处理技术进行特征提取,获取整株水稻的51个表型特征。结合深度学习,运用Faster R-CNN卷积神经网络训练模型对水稻穗数进行检测,同时使用SegNet网络框架训练得到Rice-PanicleNet模型对水稻稻穗进行分割,得到水稻穗部的二值图像,结合图像处理技术提取穗部的33个表型特征数据。对所有85个数据进行归一化处理,构建盆栽水稻稻穗鲜重、干重的预测模型,最后根据模型的决定系数R2、平均相对误差(MAPE)和相对误差绝对值的标准差(SAPE)挑选最优预测模型。预测结果表明穗部特征预测效果最好,其中效果最好的模型鲜重、干重预测值与真实值的决定系数R2分别达到0.787±0.051和0.840±0.054。本研究结合深度学习,提取了传统方法难以自动获取的穗数和穗部特征,为水稻穗重预测提供了新的思路和方法,进一步提高了水稻穗重预测的准确性。

    Abstract:

    The yield of rice is closely related to the number of panicle of rice and the weight of panicle, and accurate prediction of rice yield can accelerate the breeding speed. In order to study the relationship between rice yield and plant phenotypic characteristics, this experiment took potted rice as the research object, using visible light images combined with image processing technology for feature extraction, and obtained 51 phenotypic traits of whole rice. Combined with deep learning technology, the Faster R-CNN convolutional neural network training model was used to detect the number of rice spikes. At the same time, the Rice-PanicleNet model was trained using the SegNet network framework to segment the rice spikes to obtain the binary image of the rice spikes. , Combined with image processing technology to extract 33 phenotypic feature data of the panicle. A total of 85 phenotypic features were extracted from the image. The artificial measurement data in the experiment included the fresh weight and dry weight of potted rice panicle. Normalize all the data to build a prediction model of fresh weight and dry weight of potted rice panicles. Finally, select the most according to the model's decision coefficient R2, average relative error (MAPE) and standard deviation of relative absolute value (SAPE) Excellent prediction model. The prediction results show that the panicle characteristics prediction effect is the best, and the decision coefficients R2 of the predicted value and the real value of the model with the best effect are 0.787±0.051 and 0.840±0.054, respectively. In this study, combined with deep learning, the number of panicle and panicle characteristics that are difficult to obtain automatically by traditional methods are extracted, which provides a new idea and method for rice panicle weight prediction, and further improves the accuracy of rice panicle weight prediction.

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  • 收稿日期:2020-06-15
  • 最后修改日期:2020-06-27
  • 录用日期:2020-06-28
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