Detection of maize drought based on texture and morphological features

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

doi:10.1016/j.compag.2018.03.017

Cited by 30 publications

(15 citation statements)

References 21 publications

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“…It was also noticed that the morphological indicators of growth were affected by water stress, the plant height was reduced (Table 2). This reduction may be attributed to the decrease in the number of days to 50% tasseling (Table 1), within which the stage in which the stem elongates as well as the reduction of the content of Relative water (unpublished data) that determines the division and expansion of cells and the decrease in vegetation density in this treatment as a result of reducing the number and area of leaves (Table 3 and 4), which allowed the penetration of light into the vegetative cover, which led to the failure to give growth hormone (auxin) the opportunity in Work on the elongation of phalanges due to its photo-breaking effect negatively on plant height (Essa, 1990), This result is in agreement with the findings of Wang et al (2018), Jiang et al (2018) and Hassan (2019), who indicated a decrease in the average height of maize plants under water stress conditions. As for the decrease in the number of plant leaves when exposed to water stress, this is due to the apparent decrease in the difference in the period from planting to 50% tasseling (Table 1), in which the growth and expansion of the leaf affected by the lack of soil moisture and the high temperature, which differed between the two seasons, affected the length The length of time allocated to the growth and elongation of phalanges to hold the stem and stimulate the vegetative shoots to produce leaves, so the number of leaves emanating from the plant is reduced (Hakim et al, 2018), as well as the lower plant height (Table 2), This caused a reduction in the number of leaves, as yellow corn plants adopt the mechanism of wilting and falling of the lower leaves during water stress.…”

Section: Discussionsupporting

confidence: 85%

The Effect of Cobalt Foliar Nourishment on Reducing Water Stress for the Sweet Corn Zea Mays L. Var. Saccharata and the Characteristics of Vegetative Growth

Iedan¹,

Alag²

2021

PLANT ARCHIVES

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A field experiment was carried out with the aim of studying the effect of graduated concentrations of cobalt sprayed on plants on some growth characteristics of sweet corn crops under water stress conditions for the two autumn seasons 2018 and 2019. Use of a Randomized Complete Block Design (R.C.B.D.) according to the spilt-plot arrangement with three replications. The main-plots included three levels of water stress, which are irrigation after depleting 30, 50 and 70% of the available water, with symbols W 1 , W 2 and W 3 respectively. While the cobalt solution spraying concentrations represented 0, 4, 8 and 12 mg L -1 and symbolized as CO 0 , CO 1 , CO 2 and CO 3 respectively on the Subplots. The results showed that there were no significant differences between the two irrigation treatments after depleting 30 and 50% of the available water, with regard to the number of days to 50% tasseling, plant height, number of leaves, leaf area, plant dry weight and crop growth rate for both seasons. Whereas, irrigation after depleting 70% of the available water led to a decrease in all the studied characteristics. While the cobalt spray concentrations significantly affected most of the studied traits, except for the number of days to 50% tasseling and number of leaves. The treatment of spraying with a concentration of 12 mg L -1 significantly outperformed the rest of the treatments with the highest average plant height reaching 148.60 and 148.66 cm, leaf area 3547.00 and 3703.00 cm 2 plant -1 , plant dry weight 150.63 and 151.30 g plant -1 and crop growth rate 2.96 and 2.79 g m -2 day -1 compared with the comparison treatment CO 0 (distilled water) respectively, For the two seasons. The interaction between water stress and cobalt treatments was significant in the characteristic of plant height, leaf area and plant dry weight for both seasons. Therefore, we recommend the possibility of irrigation after depleting 50% of the available water without a significant effect on the vegetative growth characteristics, as well as the possibility of treating sweet corn plants with cobalt at a concentration of 12 mg L -1 to improve their ability to withstand water stress conditions.

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Section: Discussionsupporting

confidence: 85%

The Effect of Cobalt Foliar Nourishment on Reducing Water Stress for the Sweet Corn Zea Mays L. Var. Saccharata and the Characteristics of Vegetative Growth

Iedan¹,

Alag²

2021

PLANT ARCHIVES

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“…In the whole datasets, the drought stress identification and classification accuracy levels were higher in the color images than in the gray images. This is most likely because color information is one of the important characteristics of drought stress [40,41]. After graying, the color information of the image is lost, which makes the model more likely to be confused.…”

Section: Comparison Of the Accuracy Of Color And Gray Images Of Maizementioning

confidence: 99%

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

Mao-song

et al. 2019

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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.

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“…It needs complicated strategies to improve fusion quality. Discrete wavelet transforms technique method is used to transfer a low and high-frequency domain with local saliency method to enhance fusion image (11).…”

Section: Introductionmentioning

confidence: 99%

New algorithms to Enhanced Fused Images from Auto-Focus Images

et al. 2021

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Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contrast value because of the added edge points from the two combined images that depend on the suggested algorithms. This enhancement in edge regions is measured and reaches to double in enhancing the contrast. Different methods are used to be compared with the suggested method.

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Detection of maize drought based on texture and morphological features

Cited by 30 publications

References 21 publications

The Effect of Cobalt Foliar Nourishment on Reducing Water Stress for the Sweet Corn Zea Mays L. Var. Saccharata and the Characteristics of Vegetative Growth

The Effect of Cobalt Foliar Nourishment on Reducing Water Stress for the Sweet Corn Zea Mays L. Var. Saccharata and the Characteristics of Vegetative Growth

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

New algorithms to Enhanced Fused Images from Auto-Focus Images

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