数字农科院2.0

Learned Features Of Leaf Phenotype To Monitor Maize Water Status In The Fields

文献类型： 外文期刊

作者： Zhuang, S; Wang, P; Jiang, BR; Li, MS

作者机构：

关键词： Maize water stress; Convolutional neural network; Explainable deep learning; Feature representation; Dimensionality reduction

期刊名称： COMPUTERS AND ELECTRONICS IN AGRICULTURE

ISSN： 0168-1699

年卷期： 2020 年 172 卷

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： Water stress significantly influences normal maize growth. Fast and effective maize water stress detection is of great help to monitor the plant status and provide scientific guidance for crop irrigation. Most of the methods are based on manual measurements of soil water content, or laboratory imaging techniques, such as hyperspectral and thermal images at plant level. With the collection of 656 original maize plant images under natural environment, a novel maize leaf image dataset with different water stress levels (well-watered, reduced-watered and drought-stressed) was constructed. This paper considers maize water status detection as a fine-grained classification problem using local leaf images. Inspired by deep learning, a convolutional neural network (CNN) is applied for the first time to maize water stress recognition. In the designed CNN architecture, feature maps from different convolutional layers are merged. Through visualization and importance analysis of the mull-scale feature maps, several specific feature maps are selected as learned features, which provide a strong discrimination ability. An SVM classifier is finally trained using the feature representation as inputs. Compared with existing techniques, the proposed method achieves the satisfying classification performance with an accuracy of 88.41%. This study also provides a quantitative measure of water stress degree using a regression model. Experimental results demonstrate that the learned features perform better than hand-crafted features to detect water stress and quantify stress severity. The proposed framework can be deployed in practical applications for a non-destructive, near real-time, and automatic monitoring of plant water status in fields.

分类号：

  • 相关文献

[1]Hybrid Gene Expression Programming-Based Sensor D.ata Correlation Mining. Yang, LC, Qin, ZH, Wang, K, Deng, S. 2017

[2]Towards improved accuracy of UAV-based wheat ears counting: a transfer learning method of the ground-based fully convolutional network. Juncheng Ma,Yunxia Li,Hongjie Liu,Yongfeng Wu1,Lingxian Zhang. 2021

[3]Evaluation of Machine Learning Models for Daily Reference Evapotranspiration Modeling Using Limited Meteorological Data in Eastern Inner Mongolia, North China. Zhang H., Meng F., Xu J., Liu Z., Meng J.. 2022

[4]Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping. Zhou Shuo,Chai Xiujuan,Yang Zixuan,Wang Hongwu,Yang Chenxue,Sun Tan. 2021

[5]An automated zizania quality grading method based on deep classification model. Jingjun Cao,Tan Sun,Wenrong Zhang,Ming Zhong,Bo Huang,Guomin Zhou,Xiujuan Chai. 2021

[6]Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping. Zhou Shuo,Chai Xiujuan,Yang Zixuan,Wang Hongwu,Yang Chenxue,Sun Tan. 2021

[7]Prior Knowledge Guided Small Object Detection On High-Resolution Images. Yang, ZX, Chai, XJ, Wang, RP, Guo, WJ, Wang, WX, Pu, L, Chen, XL. 2019

作者其他论文 更多>>