To solve the problem that there are many human factors, great difficulty, and low efficiency in distinguishing quinoa seeds from weed seeds and distinguishing the quality of quinoa seeds by appearance, a method of quinoa seed detection and classification based on computer vision is proposed. In this study, convolutional neural network and Vision Transformer (ViT) were used to quickly and nondestructively classify different quinoa seeds and weed seeds. The dataset used in this experiment was 1440 sample images containing quinoa seeds and weed seeds, which were divided into training set, test set, and validation set at a ratio of 8:1:1. The training set was 1152 pieces, test set was 144, and the validation set was 144 pieces. The convolutional neural network model and ViT model based on deep learning were established. The results show that the average classification accuracies of MobileNet, VGG16, ResNet50, and ViT models used in the experiment are 93.75%, 90.97%, 93.75%, and 98.61% respectively. The accuracy of ViT classification is much higher than that of convolutional neural networks, establishing a benchmark for quinoa seed classification. This study provides a reproducible dataset construction method and a dual-imaging strategy, and demonstrates practical deployment value for automated seed grading and purity testing.

Aiming at the problem of difficulties in seed filling and seed cleaning caused by the small specific gravity, small size, and irregularity of quinoa seeds, a positive-negative pressure precision seed-metering device for quinoa seeds with a disturbed seed-filling mechanism was designed. The disturbed seed-filling mechanism can improve the seed-filling performance of the precision seed-metering device and reduce the power consumption required by the forced-draught fan. Positive pressure seed cleaning can solve the problem of pore blockage caused by small and light quinoa seeds and improve work stability. Taking Jili No. 3 seed as the research object, simulation analysis of the flow field in the air chamber of the precision seed-metering device was carried out by the Fluent 2021 R1 software. The influence of seed-sucking hole structure parameters (shape and number of seed-sucking holes, inclination angle of seed-sucking holes, and diameter of seed-sucking holes) on the flow field was analyzed by a pressure nephogram and a velocity vectogram. The optimal parameter combination was obtained as follows: a circular-cone-type shape of the seed-sucking hole, a number of 20 seed-sucking holes, a 70° inclination angle of the seed-sucking hole, and a 1 mm diameter of the seed-sucking hole. EDEM 2020 software and orthogonal test were used to optimize the design of the disturbed seed-filling mechanism. The influence of structural parameters of the disturbed seed-filling mechanism (groove radius of the disturbed seed-filling mechanism (GRDSM), arc of the disturbed seed-filling mechanism (ADSM), and position angle of the disturbed seed-filling mechanism (PADSM)) on the qualified index (I_q) of scooping seeds was analyzed. The optimal parameter combination was obtained: 1.3 mm GRDSM, 140° ADSM, and 25° PADSM. With the help of the JPS-12 test-bench, a response surface test was carried out with the qualified index (I_q), the miss index (I_miss), and the multiple index (I_mul) as test indices, and the seed-sucking negative pressure, the seed-metering device rotation speed, and the seed-falling height as test factors. The optimal working parameter combination was obtained: –3.0 kPa seed-sucking negative pressure, 15 r/min seed-metering device rotation speed, 100 mm seed-falling height. Validation tests were carried out on the optimized seed-metering device, and the results showed that the I_q was 82.5%, the I_miss was 6.5%, and the I_mul was 11%, which met the index requirements in JB/T 10293-2013 Technical conditions of single seed (precision) seeder. The results of this study can provide a reference for the design of machinery for the precision seeding of small seeds such as quinoa.