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

Abstract

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.

The effect of drought stress of sorghum grains on the textural features evaluated using machine learning

“…The tractability of biotic and abiotic stress on phenotypic characteristics of crops in the field through computer vision, machine learning, and image processing techniques have been demonstrated in several reports [35][36][37]. Crops display several mechanisms including morphological, physiological, and biochemical mechanisms to mitigate the effect of water stress resulting in different phenotypes that can be differentiated using the feature extraction method [38].…”

Developing a Modern Greenhouse Scientific Research Facility—A Case Study

“…A deep neural model with many parameters can be used for crop classification, yield prediction, and early detection of stress and disease. A considerable amount of computer vision-based work in smart agriculture focuses on plant stress detection, either as disease early detection [63] or water stress detection [64][65][66][67][68].…”

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

“…DL superiority in image recognition by automatically learning from patterns has been leveraged in plant water stress identification, with CNN becoming the standard model for automated feature extraction and transformation. An et al [110] were possibly the first who attempted to identify plant water stress in maize using pre-trained CNN: Resnet50 and Resnet120 based on three treatments of stress: optimum moisture, light drought, and moderate drought stress. The performances of the models showed between 91-98% accuracy with the fastest training time close to 8 min.…”

“…Jiang et al [111] attempted to improve the performance of the identification by introducing a Gabor filter to extract texture features from the same dataset used in [110] for the Zhengdan maize variety and reduced the dimension before feeding to the pre-trained CNN model of Alexnet. The results showed slight improvement in the model accuracy but with good adaptation to illumination changes and angle transformation.…”