Design and experimental research on disc-type seeding device for single-bud sugarcane seeds

Mechanized sugarcane seeding is one of the effective technical measures for improving seeding uniformity and operation efficiency.However,the imperfection of supporting equipment limits the promotion and application of this technology.As there are some problems in domestic sugarcane planting machines currently,such as large auxiliary labor,high labor intensity,low seeding uniformity,and large amount of seeds,a disc-type single-bud sugarcane seed metering device was innovatively designed on the basis of the analysis of physical properties of single-bud sugarcane seeds and the combination with agronomic requirements for field planting in this paper.Solidworks was used to simulate and analyze the mechanism,check the theoretical parameters,and process the test prototype.Through single-factor experiments,the effective factors affecting seeding uniformity were determined.A multi-factor orthogonal rotation test was designed,data were collected and then SPSS was used to get the optimal parameters for the seeding uniformity:the advancement speed of machine is 0.22 m/s,the number of disc seeding grooves is 10,the rotating disc speed is 0.18 r/s,and the seeding uniformity is 86.2%;the seeding uniformity was verified by field trial,and the results showed that the average seeding uniformity of the field verification was 83%,with the error 3.2% relative to the optimization result.The relative error is within 10%,indicating that the optimization result is reliable and meets the requirements of seeding uniformity,thus providing theoretical basis for the research and development of the disc-type single-bud seeding device.

Detecting sugarcane borer diseases using support vector machine

Based on the fact that great labor of artificial selection was needed after the sugarcane seeds were cut by the sugarcane cutting machine,and there was a misjudgment of the sugarcane borer diseases.SVM(support vector machine)method was proposed in this study to detect the sugarcane borer diseases.With the machine vision technology,together with threshold segmentation,filling and corrosion operation to process the three images of the same sugarcane whose interval is 120
.The classification features,minimum average gray value and the corresponding minimum gray value were selected by adaptive threshold segmentation algorithm,and removed the region which area of 1.The study used radial basis function as the kernel function of SVM,and roughly selected the range of regularization parameters of C and kernel function parameter r.Finally,it selected the optimal parameters by the grid search and the cross validation method to identify sugarcane with diseases.The test showed that correct rate of diseases and disease-free sugarcane is 96%,95.83%for the test set,so the method can effectively complete the sugarcane borer diseases detection.