Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network

文献类型： 外文期刊

第一作者： An, Jiangyong

作者： An, Jiangyong;Li, Maosong;Cui, Sanrong;Yue, Huanran;Li, Wanyi

作者机构：

关键词： drought identification; drought classification; phenotype; drought stress; maize; deep convolutional neural network; traditional machine learning

期刊名称：SYMMETRY-BASEL （ 影响因子：2.713； 五年影响因子：2.612 ）

ISSN： 2073-8994

年卷期： 2019 年 11 卷 2 期

页码：

收录情况： SCI

摘要： Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序