Analysis of the grain loss in harvest based on logistic regression

Huang, Tingkai; Li, Bingchan; Shen, Dongqin; Cao, Jie; Mao, Bo

doi:10.1016/j.procs.2017.11.426

Cited by 21 publications

(9 citation statements)

References 3 publications

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“…The step angle of the incident angle was 2°, the step size of the soil moisture was 3%, the step height of the root mean square was 0.1 cm, and the step length of the relevant length was 3 cm. Using the least squares method to perform nonlinear fitting, the specific values of A ( θ ), B ( θ ), C ( θ ) under each incident angle condition were calculated (Table 3 and Table 4) and a nonlinear regression was performed on these data [51,52] to obtain an expression of A ( θ ), B ( θ ), C ( θ ) with respect to the incidence angle.…”

Section: Resultsmentioning

confidence: 99%

Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Huang

Ding

Zou

et al. 2019

Sensors

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Soil moisture is an important aspect of heat transfer process and energy exchange between land-atmosphere systems, and it is a key link to the surface and groundwater circulation and land carbon cycles. In this study, according to the characteristics of the study area, an advanced integral equation model was used for numerical simulation analysis to establish a database of surface microwave scattering characteristics under sparse vegetation cover. Thus, a soil moisture retrieval model suitable for arid area was constructed. The results were as follows: (1) The response of the backscattering coefficient to soil moisture and associated surface roughness is significantly and logarithmically correlated under different incidence angles and polarization modes, and, a database of microwave scattering characteristics of arid soil surface under sparse vegetation cover was established. (2) According to the Sentinel-1 radar system parameters, a model for retrieving spatial distribution information of soil moisture was constructed; the soil moisture content information was extracted, and the results were consistent with the spatial distribution characteristics of soil moisture in the same period in the research area. (3) For the 0–10 cm surface soil moisture, the correlation coefficient between the simulated value and the measured value reached 0.8488, which means that the developed retrieval model has applicability to derive surface soil moisture in the oasis region of arid regions. This study can provide method for real-time and large-scale detection of soil moisture content in arid areas.

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Section: Resultsmentioning

confidence: 99%

Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Huang

Ding

Zou

et al. 2019

Sensors

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“…On certain days during this period, it is impossible to perform the harvest due to unsuitable conditions. Huang et al (2017) identified harvest time as one of the most important factors in grain loss.…”

Section: Mahdi Khani Seyyed Hossein Payman Nader Pirmoradianmentioning

confidence: 99%

Development of a Pilot Soil-Water Balance Model for Determination of Probability of a Working Day for Rice Harvesting

Khani

Payman

Pirmoradian

2021

Acta Technologica Agriculturae

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The probability of a working day is the ratio of workable days to the total available days in a working season for the intended operation. Soil moisture is the most important limiting factor in terms of rice harvesting. Therefore, to determine the PWD for this operation, a model was developed to estimate the soil moisture based on soil-water balance. To calibrate and validate the model, the soil moisture was monitored in the paddy field of the University of Guilan during the rice harvest seasons in 2017 and 2018. The calibration process was performed by applying a decreasing ratio, i.e. the senescence factor (α), at a daily evapotranspiration rate compared to that of previous day. At the validation stage, the simulated soil moisture contents were compared with measured values, which indicated a good model accuracy (NRMSE = 6.53%) of the soil moisture estimation. Therefore, this model can be used for the rice harvesting operation feasibility evaluation and PWD calculation.

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“…Agribusiness must meet the growing needs of the population for food as well as of industry for raw materials [1,2]. Modern production implies a highly efficient use of the production potential of the agro-industrial complex with technical, quality, and, especially, reliability upgrades of agricultural machinery to reduce losses and improve the quality of agricultural products [3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…This method, invented more than a century ago, involves cutting the entire crop and feeding it into a threshing machine, where it is subjected to the drum beater's impact while being dragged through a rigid grid. Up to 80% of the power is spent on threshing and grinding the straw, and about 7% of the energy is used to separate the grain from the receptacle [3,5].…”

Section: Introductionmentioning

confidence: 99%

Finite Element and Applied Models of the Stem with Spike Deformation

et al. 2021

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This article examines the static and dynamic deformation of the stem of an ear of wheat and of the individual grain. The purpose of the article is to determine the factors of influence on the ear of wheat to isolate the grain. Two stages of grain ripening are considered: in the early stage, the grain is attached to the spike by the stem; in the second, there is no stem, and the grain is attached to the ear by the scales. The ear fluctuations are considered within the Euler–Bernoulli bar theory. The developed model divides the study of the dynamics of the stem of an ear and the dynamics of the grain into two stages. The first stage studies the dynamics of the plant as a whole, while the second stage studies the dynamics of an individual grain in a moving system associated with the ear in bending and vertical fluctuation forms. The model of the ear and the grain fluctuation uses the mechanical characteristics of elastic bonds and elastic bodies, which are determined by spring stiffness, elastic moduli, etc. The results show that in the vertical forms of grain fluctuation, the frequency of fluctuation is much higher than in the bending ones. This article presents the natural fluctuation frequencies of grain at full ripeness and in the early phases of maturation, at which point it is released from the ear.

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Analysis of the grain loss in harvest based on logistic regression

Cited by 21 publications

References 3 publications

Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Development of a Pilot Soil-Water Balance Model for Determination of Probability of a Working Day for Rice Harvesting

Finite Element and Applied Models of the Stem with Spike Deformation

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