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      2. 基于深度學習的水稻表型特征提取和穗重預測研究
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        華中農業大學

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        國家自然科學基金項目(面上項目,重點項目,重大項目)


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

          水稻產量與水稻穗數和穗子重量密切相關,精確預測水稻產量可以加快育種速度。為了研究水稻產量與植株表型特征之間的關系,本實驗以盆栽水稻為研究對象,首先利用可見光圖像結合圖像處理技術進行特征提取,獲取整株水稻的51個表型特征。結合深度學習,運用Faster R-CNN卷積神經網絡訓練模型對水稻穗數進行檢測,同時使用SegNet網絡框架訓練得到Rice-PanicleNet模型對水稻稻穗進行分割,得到水稻穗部的二值圖像,結合圖像處理技術提取穗部的33個表型特征數據。對所有85個數據進行歸一化處理,構建盆栽水稻稻穗鮮重、干重的預測模型,最后根據模型的決定系數R2、平均相對誤差(MAPE)和相對誤差絕對值的標準差(SAPE)挑選最優預測模型。預測結果表明穗部特征預測效果最好,其中效果最好的模型鮮重、干重預測值與真實值的決定系數R2分別達到0.787±0.051和0.840±0.054。本研究結合深度學習,提取了傳統方法難以自動獲取的穗數和穗部特征,為水稻穗重預測提供了新的思路和方法,進一步提高了水稻穗重預測的準確性。

          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
        • 錄用日期:2020-06-28
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