Healthy seedlings transplanting is an important process in the production of vegetables and economic crops, and the transplanting quality directly affects crop yield. Automatic seedlings transplanting can improve the transplanting efficiency of seedlings. A physical prototype of potted seedling automatic transplanting with a conveyor and a transplanting end-effector was developed in the previous study. This work proposed an automatic transplanting method of healthy potted seedlings, which mainly included a detection part of seedlings growth status and a visual servo control part with the purpose of automatic transplanting seedlings high-efficiently. The seedlings and the tray cell were simultaneously detected for identifying healthy seedlings, damaged seedlings and empty cells using a machine vision algorithm when the tray was moving on the conveyor line. The visual servo model was applied to enable the collaborative operation of the machine vision and the end-effector for determining the position and attitude of grasping seedlings. The experimental results showed that the accuracy rates of the identification of empty tray cells, healthy seedling and unhealthy seedling were 96.42%, 98.77% and 89.95%, respectively. Under the successful identification of the healthy seedling, the accuracy rate of grasping seedling was 96.38%. It indicated that the proposed method can effectively transplant seedlings. Keywords: seedling recognition, automatic transplanting, computer vision, transplanting system DOI: 10.25165/j.ijabe.20211406.6638 Citation: Jin X, Tang L M, Ji J T, Wang C L, Wang S S. Potential analysis of an automatic transplanting method for healthy potted seedlings using computer vision. Int J Agric & Biol Eng, 2021; 14(6): 162–168.

To determine the optimal operating parameters of the single-longitudinal-axial-flow threshing device for wheat harvesters under high feed rate conditions and lay a theoretical and methodological foundation for the subsequent development of a specialist system for threshing device parameter optimization, this study constructed a discrete element model (DEM) of wheat plants at harvest stage in EDEM software based on the Hertz–Mindlin with bonding contact model, combined with the discrete element parameters of wheat plants calibrated by a texturometer. Simulation experiments were designed and conducted to optimize the operating parameters of the threshing device, and a methodology that employs simulation experiments to obtain optimal parameter combinations under different operating environments was proposed, which was applied to the development of a specialist system for threshing device parameter optimization. Taking cylinder speed, cylinder inclination and threshing gap as experimental factors, and unthreshed rate, separation loss rate and breakage rate as evaluation indexes, single-factor and Box–Behnken simulation experiments were carried out. Mathematical models were established to reveal the correlation between evaluation indexes and influencing parameters, and then the optimal parameter combination was solved. The results showed that the threshing device achieved the optimal comprehensive operating performance at a cylinder speed of 850 r/min, a cylinder inclination of 6° and a threshing gap of 20 mm, with an unthreshed rate of 1.575%, a separation loss rate of 0.158% and a breakage rate of 0.509%. Bench validation tests indicated that the relative errors between simulated and measured indexes were all less than 5%, verifying the reliability of the established DEM. Compared with bench tests, the simulation experiment method in this study ensured high accuracy while significantly reducing the time and labor costs of tests. The obtained optimal operating parameters and the proposed simulation optimization methodology provide core data support and technical reference for the development of a specialist system for parameter optimization of the threshing device of wheat harvesters.

To investigate the dynamic threshing and separation process of white radish seed pods using the Discrete Element Method (DEM) and improve the accuracy of DEM simulations, a study on parameter calibration for the DEM model of white radish seed pods was carried out through experimental and simulation methods. Firstly, the static friction coefficient and rolling friction coefficient between seed pods and steel plates and between seed pods themselves, as well as restitution coefficient between seed pods and steel plates, were measured using the inclined plane method and the flat plate collision method. The angle of repose test for white radish seed pods was conducted using the funnel accumulation method, and the contour of the angle of repose image was fitted by Origin 2018 software, yielding a measured angle of repose of 31.03°. Key contact parameters between white radish seed pods and operating equipment were determined using the Plackett–Burman design and the steepest ascent method, and the model reliability was verified through the angle of repose test. The results indicated that restitution coefficient, static friction coefficient, and rolling friction coefficient between white radish seed pods and operating equipment were 0.5, 0.488, and 0.025, respectively. The optimal values of restitution coefficient, static friction coefficient, and rolling friction coefficient between white radish seed pods themselves were 0.4, 0.219, and 0.055, respectively. In the angle of repose verification test, the relative error between the simulated and measured values was 2.56%. A DEM-based threshing simulation of white radish seed pods was conducted, achieving a threshing efficiency of 96.47%, with a relative error of 2.24% compared to the actual threshing efficiency. The DEM model of white radish seed pods showed good agreement with the actual situation, and the calibrated DEM simulation parameters were reliable, providing a reference for the DEM simulation studies of white radish seed pods.