基于Self-Attention-BiLSTM网络的西瓜种苗叶片氮磷钾含量高光谱检测方法  

Hyperspectral Non-destructive Detection of Nitrogen,Phosphorus and Potassium Content of Watermelon Seedling Leaves Based on Self-Attention-BiLSTM Network

作　　者：徐胜勇[1,2] 刘政义 黄远 曾雨 别之龙[3] 董万静 XU Shengyong;LIU Zhengyi;HUANG Yuan;ZENG Yu;BIE Zhilong;DONG Wanjing(College of Engineering,Huazhong Agricultural University,Wuhan 430070,China;Agricultural Genomics Institute at Shenzhen,Chinese Academy of Agricultural Sciences,Shenzhen 518000,China;College of Horticulture and Forestry Sciences,Huazhong Agricultural University,Wuhan 430070,China)

机构地区：[1]华中农业大学工学院,武汉430070 [2]中国农业科学院深圳农业基因组研究所,深圳518000 [3]华中农业大学园艺林学学院,武汉430070

出　　处：《农业机械学报》2024年第8期243-252,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2019YFD1001901);湖北省重点研发计划项目(2021BBA239);HZAU-AGIS交叉基金项目(SZYJY2022006);中央高校基本科研业务费专项资金项目(2662022YLYJ010);国家西甜瓜产业技术体系项目(CARS-25)。

摘　　要：元素含量无损检测技术可以为植物生长发育的环境精准调控提供关键实时数据。以西瓜苗为例,提出了一种基于图谱特征融合的氮磷钾含量深度学习检测方法。首先,使用高光谱仪拍摄西瓜苗叶片的高光谱图像,使用连续流动化学分析仪测定叶片的3种元素含量。然后,采用基线偏移校正(BOC)叠加高斯平滑滤波(GF)的光谱预处理方法和随机森林算法(RF)建立预测模型,基于竞争性自适应重加权采样(CARS)和连续投影算法(SPA)2种算法初步筛选出特征波长,再综合考虑波长数和建模精度设计了一种最优波长评价方法,将波长数进一步减少到3~4个。最后,提取使用U-Net网络分割的彩色图像颜色和纹理特征,和光谱反射率特征一起作为输入,基于自注意力机制-双向长短时记忆(Self-Attention-BiLSTM)网络构建了3种元素含量的预测模型。实验结果表明,氮磷钾含量预测的R2分别为0.961、0.954、0.958,RMSE分别为0.294%、0.262%、0.196%,实现了很好的建模效果。使用该模型对另2个品种西瓜进行测试,R2超过0.899、RMSE小于0.498%,表明该模型具有很好的泛化性。该高光谱建模方法使用少量波长光谱即实现了高精度检测,在精度和效率上达成了很好的平衡,为后续便携式高光谱检测装备开发奠定了理论基础。Element content non-destructive testing technology can provide key real-time data for precise environmental regulation of plant growth and development.Taking watermelon seedlings as an example,a deep learning detection method based on graph feature fusion for nitrogen,phosphorus,and potassium content was proposed.Firstly,high-resolution hyperspectral images of watermelon seedling leaves were captured by using a hyperspectral image.The content of the three elements in the leaves was determined by using a continuous flow chemical analyzer.Then,the BOC-GF spectral preprocessing method and the RF algorithm were used to establish a prediction model.Based on the CARS and SPA algorithms,feature bands were preliminarily selected.Then,considering the number of bands and modeling accuracy,an optimal band evaluation method was designed to further reduce the number of bands to 3~4.Finally,the colour and texture features of the colour images segmented by using the U-Net network were extracted and used as inputs along with the spectral reflectance features to construct a prediction model for the three elemental contents based on the Self-Attention-BiLSTM network.The experimental results showed that the R~(2)values for predicting nitrogen,phosphorus,and potassium content were 0.961,0.954,and 0.958,respectively,with corresponding RMSE values of 0.294%,0.262%,and 0.196%.These results indicated a high level of modeling accuracy.Using this model to test two other varieties of watermelon,the R~2 values exceeded 0.899 and the RMSE values were less than 0.498%,indicating that the model had excellent generalization ability.This hyperspectral modeling method achieved high accuracy detection with a small number of spectral bands,striking a good balance between precision and efficiency.It laied a solid theoretical foundation for the development of portable hyperspectral detection equipment in the future.

关 键 词：西瓜苗叶片 元素含量 无损检测 自注意力机制 双向长短时记忆网络 高光谱 

分 类 号：S56[农业科学—作物学]