Simulation of negative pressure behavior using different shapes and positions of pressure inlet and seed hole diameters using ANSYS-CFX to optimize the structure of a pneumatic metering device designed for wheat

Abstract: the pneumatic precision metering device for wheat was innovated, designed and tested under laboratory conditions. Very good results were then obtained according to the precision seeding criteria based on quality of feed index (QFI), miss index (MIS) and multiple index (MULI). To optimize the structure of the design for gas stability before manufacturing, ANSYS-CFX was involved. Gas behavior was simulated under different shapes and positions for pressure inlet along with different seed hole diameters. According to the structure determined in ANSYS-CFX, the seed hole of 1.8 mm and cylindrical shape of pressure inlet was found to be the best among others, accordingly the deice was then manufactured and tested under laboratory and field conditions. The results from laboratory and field experiments were found to be in conformity with these of simulation, which indicated that ANSYS-CFX is a powerful and highly accurate application in optimizing the structure of such designs.   

Parameter Estimation for Partial Differential Equations by Collage-Based Numerical Approximation

The inverse problem of using measurements to estimate unknown parameters of a system often arises in engineering practice and scientific research. This paper proposes a Collage-based parameter inversion framework for a class of partial differential equations. The Collage method is used to convert the parameter estimation inverse problem into a minimization problem of a function of several variables after the partial differential equation is approximated by a differential dynamical system. Then numerical schemes for solving this minimization problem are proposed, including grid approximation and ant colony optimization. The proposed schemes are applied to a parameter estimation problem for the Belousov-Zhabotinskii equation, and the results show that the proposed approximation method is efficient for both linear and nonlinear partial differential equations with respect to unknown parameters. At worst, the presented method provides an excellent starting point for traditional inversion methods that must first select a good starting point

Design and test of a pneumatic precision metering device for wheat

The objective of this study was to apply the precision metering on wheat seeding to overcome seed damage, seed loss and non-uniform distribution.  Accordingly, a prototype of the pneumatic precision metering device for wheat was developed.  The performance of the device, including quality of feed index (QFI), multiple index (MULI), miss index (MISI) and seed rate expressed in number of kernels per meter length (KPM), was investigated under laboratory conditions in Wuhan using a test stand with camera system.  The results revealed that the rotating speed (RS) and negative pressure (NP) and their interactions had a significant effect on these variables.  The maximum QFI (92.98%) was obtained at rotating speed of 19.0 rpm and negative pressures of 2.5 kPa with MULI and MISI of 2.01% and 5.09%, respectively.  However, the seed rate (KPM) was less than the recommended compared to previous hypothesis.  The best seed rate was 53 KPM producing QFI of 89.11% with MULI and MISI of 9.00% and 1.88%, respectively at rotating speed of 34 rpm and negative pressure of 4.5 kPa.  The recommended seed rates estimated at 40 KPM and 53 KPM for 12 cm and 15 cm row spacing respectively were achieved at a range of RS and NP with QFI ranging between 84.57 to 89.11%.  The study demonstrated that wheat could be seeding within an acceptable precisely range by pneumatic precision metering device. Keywords: wheat, experiments, performance indices, pneumatic precision metering device

Dynamic analysis for kernel picking up and transporting on a pneumatic precision metering device for wheat

The objective of this study was to theoretically investigate the factors affecting kernels during picking up and transporting stage using a pneumatic precision metering device designed especially for wheat precision seeding and correlates findings with the results from practical testing under laboratory conditions using a test stand with camera system.  The results from dynamic analysis were found to be corresponding with that of the laboratory testing.  The findings revealed that the performance indices, such as quality of feed index (QFI), multiple index (MULI) and miss index (MISI), were obviously influenced by changing the negative pressure force FQ and rotating speed ω.  The result from test stand highlighted that when the negative pressure increased the QFI increased, MULI increased and MISI decreased, however, the QFI decreased and MISI increased with increasing the rotating speed.  The dynamic analysis likewise revealed that increasing the friction index tanαg by choosing a suitable material with high friction angle αg for seed plate as well as enlarging the seed hole diameter could improve the efficiency of the negative pressure force FQ.   Keywords: wheat, kernel, picking up, transportation, dynamic model, precision metering devic

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

FAULT CHARACTERISTICS ANALYSIS OF 2BFQ-6 PRECISION SEEDER FOR RAPESEED

A newly-developed precision seeder,model 2BFQ-6,combined multiple field tasks of seeding rapeseed,including straw chopping,rotary tillage,fertilizing,precision sowing,and straw covering into a single operation,has been expanded and applied in major rapeseed producing areas of China. Unfortunately,its reliability does not meet the need of production,which restricts its popular. In this paper,product reliability test and fault analysis of the combined seeder was proposed to improve its product reliability. A brand new seeder was conducted to work in rice stubble field lasting longer than 30 hectare in Jianli County,Hubei Province,in 2013. Trouble-free working time and area,phenomenon of the fault,repair time and the cause of the fault of the seeder in the course of the work were recorded and used for fault characteristics analysis. The results show that the key components of the seeder,like the main frame,gearbox and rotary drive axis have not fault yet,even if they are undertaking the most of load in working. The facts prove design reliability of the seeder is relatively high. By contrast,the glitches frequently appear initially,which is certainly abnormal. It caution that manufacturing process should be improved,especially for sprockets assembling and welding. Another significant finding is that the bolted connection of rotary blade and base would be loosen at a period of 5. 9-hour,which lead to the fault of the rotary tillage unit. Fault analysis indicates that the period of early fault is 30 hours and the fault rate conform to the feature of a bathtub curve. Real productivity of the seeder is 0. 36 hm~2/h,one working shift productivity is 2. 16 hm~2/d and the availability coefficient of the seeder was nearly 70%. When assembling and welding process are improved,weak components and unreasonable belt drive structure are revised and the connection of rotary blade and base are preventative maintained,the availability coefficient will be increased more than 97% and the real productivity of the seeder will be promoted to 0. 45 hm~2/h

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Foam nickel-PDMS composite film based triboelectric nanogenerator for speed and acceleration sensing

As a new energy conversion technology, triboelectric nanogenerator (TENG) can use the coupling of triboelectrification and electrostatic induction effect to convert tiny mechanical energy into electrical energy, powering small electronic devices. In this paper, a vibration sensing triboelectric nanogenerator (V-TENG) based on a foam nickel-PDMS composite film was prepared, which can convert low frequency and small-amplitude mechanical energy into electrical energy, and the open circuit voltage of V-TENG can reach 3.6V at a vibration frequency of 4 Hz. In addition, the V-TENG can be used as a self-powered speed/acceleration sensor to detect speed changes in the range of 0.3 m/s to 1.5 m/s and acceleration changes in the range of 3 m/s2 to 13 m/s2

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages