التفاصيل البيبلوغرافية
| العنوان: |
基于堆栈稀疏自编码器的小麦赤霉病高光谱遥感检测. (Chinese) |
| Alternate Title: |
Hyperspectral remote sensing detection of Fusarium head blight in wheat based on the stacked sparse auto-encoder algorithm. (English) |
| المؤلفون: |
林芬芳, 陈星宇, 周维勋, 王 倩, 张东彦 |
| المصدر: |
Acta Agronomica Sinica; 2023, Vol. 49 Issue 8, p2275-2287, 13p |
| Abstract (English): |
Fusarium head blight (FHB) has the characteristics of rapid onset and short cycle. The deep learning feature extraction method was used to establish a disease severity detection model to provide guidance for the prevention and control of FHB. The hyperspectral data of wheat ears from flowering to maturity under three varieties from 2018 to 2020 were collected. The spectral curves of wheat ears were obtained by morphological processing and multi-source scattering correction. Then spectral features of FHB were extracted by stacked sparse auto-encoder (SSAE), combined with Softmax classifier and the partial least squares regression method to detect FHB. Through pre-training, the two-layer SSAE model with 12--6 neurons performed better, the mean square error of the model was lower, and the characteristics of each disease level were significantly different. The deep learning features extracted by the trained SSAE model were the basis of the establishment of FHB disease severity level discrimination model and severity prediction model. The overall accuracy and Kappa coefficient of the model were 88.2% and 0.84, respectively, and the accuracy was the highest for the variety of 'Huaimai 35'. The prediction coefficient of determination (R2) and root mean square error (RMSE) of the model for the test set of all varieties were 0.927 and 0.062 in the severity prediction model, respectively, and R2 for each variety was around 0.95. The FHB prediction model based on SSAE deep learning features has higher accuracy than those with several common FHB spectral indices. Hyperspectral remote sensing had the characteristics of large amount of data and many spectral bands. The stack sparse auto-encoder builded a more complex model by adding the limiting conditions of sparse representation to the auto-encoder model, and increasing the number of hidden layers and hidden neurons. The extracted spectral features can better reflect the spectral characteristics of FHB in all aspects, so the detection model of FHB constructed by using these features has higher accuracy, which provides a reference for timely and accurate monitoring of FHB. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
小麦赤霉病具有发病快、周期短的特点, 利用深度学习特征提取方法建立病害严重度检测模型, 可为小麦赤 霉病的防治提供科学指导。研究于2018--2020年间采集3 个品种小麦在扬花期、灌浆期和成熟期的麦穗高光谱数据, 通过形态学处理去除麦芒, 提取出麦穗光谱曲线, 使用多源散射校正对光谱进行去噪处理, 再采用堆栈稀疏自编码 器(Stacked Sparse Auto-encoder, SSAE)提取小麦赤霉病的光谱特征，利用该特征分别结合Softmax分类器和偏最小二 乘回归方法构建小麦赤霉病严重度判别和预测模型。通过预训练，具有12~6个神经元的双层SSAE模型表现较好，模 型均方误差更低, 而且各个病害等级的特征差异明显; 以训练的 SSAE 模型提取的深度学习特征为基础分别建立赤 霉病严重度等级判别模型和严重度预测模型, 在严重度等级判别的分类结果中, 模型的总体精度和 Kappa 系数分别 为 88.2%和 0.84, 其中"淮麦35"品种的总体精度最高; 在严重度预测模型中, 模型对所有品种测试集的预测决定系数 和均方根误差分别为0.927和 0.062, 对各品种的预测决定系数均在0.95左右; 相比常见的几种小麦赤霉病光谱指数, 基于 SSAE 深度学习特征的赤霉病预测模型精度更高。高光谱遥感数据量大、光谱波段多, 堆栈稀疏自编码器通过 在自编码器模型中加入稀疏表示的限定条件, 并增加隐含层数及隐含神经元数来构建更为复杂的模型, 所提取的光 谱特征更能全方面地体现小麦赤霉病的光谱特征, 利用该特征构建的小麦赤霉病检测模型具有更高的精度, 可为精 准监测小麦赤霉病提供科学依据 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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