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

• Published in: Symmetry (Basel) 2019-02, Vol.11 (2), p.256
• Main Authors: An, Jiangyong, Li, Wanyi, Li, Maosong, Cui, Sanrong, Yue, Huanran
• Format: Article
• Language: English
• Subjects: Abiotic stress; Accuracy; Agricultural production; Artificial neural networks; Classification; Color imagery; Corn; Crop diseases; Crop growth; Datasets; Decision trees; deep convolutional neural network; Deep learning; Digital cameras; Digital imaging; Drought; drought classification; drought identification; drought stress; Experiments; Feature extraction; Identification; Image classification; Irrigation; Loam soils; Machine learning; maize; Neural networks; phenotype; Plant diseases; Plant growth; Production methods; Seeds; Sensors; Support vector machines; traditional machine learning
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym11020256

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.