Kernel crack characteristics for X-ray computed microtomography(μCT)and their relationship with the breakage rate of maize varieties

The most significant problem of maize grain mechanical harvesting quality in China at present is the high grain breakage rate(BR).BR is often the key characteristic that is measured to select hybrids desirable for mechanical grain harvesting.However,conventional BR evaluation and measurement methods have challenges and limitations.Microstructural crack parameters evaluation of maize kernel is of great importance to BR.In this connection,X-ray computed microtomography(μ-CT)has proven to be a quite useful method for the assessment of microstructure,as it provides important microstructural parameters,such as object volume,surface,surface/volume ratio,number of closed pores,and others.X-ray computed microtomography is a non-destructive technique that enables the reuse of samples already measured and also yields bidimensional(2D)cross-sectional images of the sample as well as volume rendering.In this paper,six different maize hybrid genotypes are used as materials,and the BR of the maize kernels of each variety is tested in the field mechanical grain harvesting,and the BR is used as an index for evaluating the breakage resistance of the variety.The crack characteristic parameters of kernel were detected by X-ray micro-computed tomography,and the relationship between the BR and the kernel crack characteristics was analyzed by stepwise regression analysis.Establishing a relationship between crack characteristic parameters and BR of maize is vital for judging breakage resistance.The results of stepwise multiple linear regression(MLR)showed that the crack characteristics of the object surface,number of closed pores,surface of closed pores,and closed porosity percent were significantly correlated to the BR of field mechanical grain harvesting,with the standard partial regression coefficients of–0.998,–0.988,–0.999,and–0.998,respectively.The R2 of this model was 0.999.Results validation showed that the Stepwise MLR Model could well predict the BR of maize based on these four variables.

Gravel hardness effect on compaction characteristics of gravelly soil

The compaction characteristics of gravelly soil are affected by gravel hardness.To investigate the evolution and influencing mechanism of different gravel hardness on the compaction characteristics of gravelly soil,heavy compaction tests and crushing tests were conducted on gravelly soils with gravels originated from hard,soft and extremely soft rocks.According to orthogonal experiments and variance analysis,it was found that hardness has a significant impact on the maximum dry density of gravelly soil,followed by gravel content,and lastly,moisture content.For gravel compositions with an average saturated uniaxial compressive strength less than 60 MPa,the order of compacted maximum dry density is soft gravels>hard gravels>extremely soft gravels.Each type of gravelly soil has a threshold for gravel content,with 60%for hard and soft gravels and 50%for extremely soft gravels.Beyond these thresholds,the compacted dry density decreases significantly.There is a certain interaction between hardness,gravel content,and moisture content.Higher hardness increases the influence of gravel content,whereas lower hardness increases the influence of moisture content.Gravelly soils with the coarse aggregate(CA)between 0.7 and 0.8 typically achieve higher dry densities after compaction.In addition,the prediction equations for the particle breakage rate and CA ratio in the Bailey method were proposed to estimate the compaction performance of gravelly soil preliminarily.The results further revealed the compaction mechanism of different gravelly soils and can provide reference for subgrade filling construction.

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Breakage rate is one of the most important indicators to evaluate the harvesting performance of a combine harvester.It is affected by operating parameters of a combine such as feeding rate,the peripheral speed of the threshing cylinder and concave clearance,and shows complex non-linear law.Real-time acquisition of the breakage rate is an effective way to find the correlation of them.In addition,real-time monitoring of the breakage rate can help the driver optimize and adjust the operating parameters of a combine harvester to avoid the breakage rate exceeding the standard.In this study,a real-time monitoring method for the grain breakage rate of the rice combine harvester based on machine vision was proposed.The structure of the sampling device was designed to obtain rice kernel images of high quality in the harvesting process.According to the working characteristics of the combine,the illumination and installation of the light source were optimized,and the lateral lighting system was constructed.A two-step method of“color training-verification”was applied to identify the whole and broken kernels.In the first step,the local threshold algorithm was used to get the edge of kernel particles in a few training images with binary transformation,extract the color spectrum of each particle in color-space HSL and output the recognition model file.The second step was to verify the recognition accuracy and the breakage rate monitoring accuracy through grabbing and processing images in the laboratory.The experiments of about 2300 particles showed that the recognition accuracy of 96%was attained,and the monitoring values of breakage rate and the true artificial monitoring values had good trend consistency.The monitoring device of grain breakage rate based on machine vision can provide technical supports for the intellectualization of combine harvester.