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作物学报

作物学报 ›› 2023, Vol. 49 ›› Issue (8): 2275-2287.doi: 10.3724/SP.J.1006.2023.21060

所属专题： 小麦：耕作栽培·生理生化

• 耕作栽培·生理生化 • 上一篇    下一篇

基于堆栈稀疏自编码器的小麦赤霉病高光谱遥感检测

林芬芳1,2,3(), 陈星宇1, 周维勋1, 王倩2, 张东彦2,*()   

  1. 1 南京信息工程大学遥感与测绘工程学院, 江苏南京 210044
    2 农业生态大数据分析与应用技术国家地方联合工程研究中心(安徽大学), 安徽合肥 230601
    3 黄河中下游数字地理技术教育部重点实验室(河南大学), 河南开封 475004
  • 收稿日期:2022-09-07 接受日期:2023-02-10 出版日期:2023-08-12 网络出版日期:2023-02-28
  • 通讯作者: 张东彦
  • 作者简介:E-mail: linfenfang@126.com
  • 基金资助:
    国家自然科学基金项目(42271364);江苏省科技计划项目(BK20211287);黄河中下游数字地理技术教育部重点实验室(河南大学)开放课题项目(GTYR202104)

Hyperspectral remote sensing detection of Fusarium head blight in wheat based on the stacked sparse auto-encoder algorithm

LIN Fen-Fang1,2,3(), CHEN Xing-Yu1, ZHOU Wei-Xun1, WANG Qian2, ZHANG Dong-Yan2,*()   

  1. 1 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
    2 National Engineering Research Center for Agro-Ecological Big Data Analysis and Application (Anhui University), Hefei 230601, Anhui, China
    3 Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Kaifeng 475004, Henan, China
  • Received:2022-09-07 Accepted:2023-02-10 Published:2023-08-12 Published online:2023-02-28
  • Contact: ZHANG Dong-Yan
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(42271364);Science and Technology Plan in Jiangsu Province(BK20211287);Open Fund of Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University)the Ministry of Education(GTYR202104)

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摘要：

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。

关键词: 赤霉病, 堆栈稀疏自编码器, 高光谱, 检测, 小麦

Abstract:

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.

Key words: Fusarium head blight, stacked sparse auto-encoder, hyperspectral, detection, wheat

图1

研究方法流程图"

图2

麦穗形态学处理 (a) 原始影像; (b) 形态学处理后影像; (c) 二值化影像。"

图3

YDbDr空间下麦穗的Dr分量(a)和麦穗的OTSU阈值分割结果(b)"

表1

小麦赤霉病病害严重度等级"

级别
Grade		 病害严重度DI值范围
Range of DI		 病害等级
Grade of disease
1 0<DI<0.05 健康 Healthy
2 0.05≤DI<0.20 轻度 Slight
3 0.20≤DI<0.50 中度 Moderate
4 DI≥0.50 重度 Serious

图4

堆栈稀疏自编码器结构 图中第1个框表示数据输入层, 第2个框表示第1层SAE, 第3个框表示第2层SAE, 第4个框表示输出层, 第5个框表示连接的分类器或回归方法。"

图5

不同品种和病害等级下经MSC校正后的小麦麦穗反射光谱曲线 图中不同颜色代表不同的病害等级, 其中黑色表示健康, 红色表示轻度, 蓝色表示中度, 绿色表示重度。"

图6

各层不同神经元数量组合下模型的训练均方误差"

图7

每层SAE提取的各病害等级特征 (a) 第1层; (b) 第2层。不同线型代表不同的病害等级, 其中双点划线表示健康, 短划线表示轻度, 直线表示中度, 点线表示重度。"

图8

不同品种下小麦赤霉病严重度等级判别模型的总体精度和Kappa系数"

图9

不同品种下各病害等级的生产者精度(a)和用户精度(b)"

图10

预测模型精度 (a) 所有品种测试集; (b) 西农979测试集; (c) 淮麦35测试集; d) 漯麦10测试集。"

表2

小麦赤霉病光谱指数及其计算公式"

光谱指数
Spectral index		 光谱指数全称
Full name of spectral index		 计算公式
Formula of calculation
REHBI Red-edge head blight index $\frac{\left( 842-665 \right)\times \left( {{R}_{\text{Re3}}}-{{R}_{\text{R}}} \right)-\left( 783-665 \right)\times \left( {{R}_{\text{NIR}}}-{{R}_{\text{R}}} \right)}{\text{2}}$
FDI Fusarium disease index $\frac{{{R}_{560}}-{{R}_{663}}}{{{R}_{560}}+{{R}_{663}}}$
WSI Wheat scab index $\frac{\text{S}{{\text{D}}_{450-488}}-\text{S}{{\text{D}}_{500-540}}}{\text{S}{{\text{D}}_{450-488}}+{{\operatorname{SD}}_{500-540}}}$
FCI Fusarium classification index $\frac{2\left( {{R}_{668}}-{{R}_{471}} \right)-{{R}_{539}}}{4}$
HBI Head blight index ${{R}_{550-560}}-{{R}_{665-675}}$

图11

深度学习特征和各小麦赤霉病光谱指数预测精度比较"

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