Early detection of water stress in maize based on digital images

Zhuang, Shuo; Wang, Ping; Jiang, Boran; Mao-song, LI; Gong, Zaiwu

doi:10.1016/j.compag.2017.06.022

Cited by 52 publications

(24 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In comparison experiments with traditional machine learning, we used the same dataset as in a previous study [9]. The dataset is a sub-dataset of the seedling stage and contained 656 images including 219 optimum moisture images, 218 light drought stress images, and 219 moderate drought stress images.…”

Section: Training Processmentioning

confidence: 99%

“…Referring to previous work by our team [9], two accuracy measures were proposed to evaluate the effectiveness of the detection model: the accuracy of drought stress identification (DI) and the accuracy of drought stress classification (DC). These were defined as = + × 100%…”

Section: Training Processmentioning

confidence: 99%

“…We compared DCNN with the traditional machine learning method (GBDT) previously published by our team using the same dataset as them [9]. The test confusion matrix is shown in Table 3.…”

Section: A Comparison Between Dcnn and Traditional Machine Learningmentioning

confidence: 99%

“…Traditional drought stress severity monitoring is based on soil moisture sensors [6], which have low efficiency and limited indirect and spatial area [7,8]. Maize plants develop different physiological mechanisms to mitigate the impact of drought stress, causing a series of phenotypic changes, such as changes in leaf color and texture [9], leaf rolling, a decreased leaf extension rate, and plant dwarfing [10,11]. Thus, maize phenotypic variation is a direct manifestation of maize drought stress.…”

Section: Introductionmentioning

confidence: 99%

“…Two DCNN models were applied to detect maize drought stress. We also compared the identification accuracy of DCNN and traditional machine learning (GBDT) [9]. The main contributions of this paper are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Mao-song

et al. 2019

Symmetry

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Training Processmentioning

confidence: 99%

Section: Training Processmentioning

confidence: 99%

“…We compared DCNN with the traditional machine learning method (GBDT) previously published by our team using the same dataset as them [9]. The test confusion matrix is shown in Table 3.…”

Section: A Comparison Between Dcnn and Traditional Machine Learningmentioning

confidence: 99%