Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R²c) for the training set and (R²p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.

In seed processing for commercial production, the collision or friction between mechanical parts and seed is inevitable. These collisions or friction can cause cracks on the surface of the seed, which can affect germination rates and ultimately reduce crop yields. Using the difference of the seed motion trajectories with different crack degrees in the magnetic field, the grading of the surface crack seed can be realized. In this study, the motion law of seed in a non-uniform magnetic field was analyzed by taking the delinted cottonseed as an object. A magnetic separation device for crop seeds was developed. This device was essentially composed of the feed throat, magnetic roller, conveyor belt, and variable-frequency adjustable-speed motor. The magnetic powder adhering seeds enter the magnetic separation device through the feed throat and are conveyed to the magnetic roller through the conveyor belt. The magnetic roller has different adsorption forces on seeds with different degrees of cracking, and seeds fall into different outlets, thus completing the grading of the seeds. By adjusting working parameters such as the magnetic field strength and the speed of the conveyor belt, magnetic separation could be made. In order to verify the grading effect, the magnetic separation accuracy was taken as the inspection index, the magnetic powder’s mesh number, the mass mixing ratio between magnetic powder and delinted cottonseed, and the rotation speed of the magnetic roller were taken as the inspection factors. The response surface methodology was used to optimize the working parameters of the experimental device. The results showed that the optimal process parameters of the magnetic separation device were as follows: the number of magnetic powder meshes was 250, the mass mixing ratio between magnetic powder and delinted cottonseed was 1:20, and when the rotational speed of the magnetic separation roller was 20 r/min, the detection rate reached 92.5%. The designed magnetic separation device can realize the non-destructive batch detection of seed surface cracks with high work efficiency and has guiding significance for the high-precision and low-damage classification detection and classification of commercial seed. Keywords: magnetic separation; delinted cottonseed; surface crack; response surface design DOI: 10.25165/j.ijabe.20231606.7509 Citation: Du X W, Wang G D, Pang J, Li P F, Jin X, Mao W H. Development of the magnetic separation device for surface cracks of delinted cottonseed. Int J Agric & Biol Eng, 16(6): 11-19.