Calibration Method Research of Structured-light Sensor Camera System for Soil Surface Roughness Measurement

2018 ◽  
Author(s):  
Xiaoju Yang ◽  
Hang Xie ◽  
Yitao Liao ◽  
Ning Dai ◽  
Guangyin Gao ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Structured Light ◽  
Soil Surface ◽  
Calibration Method ◽  
Roughness Measurement ◽  
Camera System ◽  
Surface Roughness Measurement ◽  
Light Sensor ◽  
Soil Surface Roughness

Ground-based imaging system for soil surface roughness measurement

2004 ◽  
Author(s):  
Zhiling Long ◽  
Ping-Rey Jang ◽  
Jiann-Cherng Su ◽  
Yun Sun ◽  
J. A. Thomasson ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Imaging System ◽  
Soil Surface ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Soil Surface Roughness

Surface Roughness Measurement of Thermal Barrier Coating using Terahertz Waves

2017 ◽  
Vol 137 (3) ◽  
pp. 147-152 ◽  
Author(s):  
Tetsuo Fukuchi ◽  
Norikazu Fuse ◽  
Mitsutoshi Okada ◽  
Tomoharu Fujii ◽  
Maya Mizuno ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Thermal Barrier Coating ◽  
Thermal Barrier ◽  
Roughness Measurement ◽  
Terahertz Waves ◽  
Surface Roughness Measurement ◽  
Barrier Coating

scholarly journals Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion

2020 ◽  
Vol 12 (1) ◽  
pp. 232-241
Author(s):  
Na Ta ◽  
Chutian Zhang ◽  
Hongru Ding ◽  
Qingfeng Zhang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Soil Surface ◽  
Rainfall Events ◽  
Tillage Practices ◽  
Surface Microtopography ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
The Difference ◽  
Sheet Erosion

AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

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