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At present, the sorting of agricultural products in China mainly relies on manual labour, which results in low efficiency, and the development of corresponding automatic equipment lags behind. The grasping method based on machine vision has been widely used in industry, and can provide a reference for the automatic sorting of agricultural products. In this paper, an automatic sorting device for agricultural products was designed. The grasping mechanism adopted a 4-degree-of-freedom (4-DOF) manipulator, and the machine vision control system adopted a monocular camera, which can realize the positioning and classification of the grasp-target. First, the geometric model of the manipulator was established, and the kinematics model of the manipulator was established via the Denavit-Hartenberg (D-H) parameter method. Next, the kinematics analysis and verification were carried out. Then, Zhang Zhengyou calibration method was used to calibrate the camera. An image processing method based on histogram correction was proposed. Based on this, a target positioning algorithm based on the pinhole imaging principle and a target classification algorithm based on the area threshold were established. Finally, an automatic sorting test platform for agricultural products using a visual servo was built. Target classification, positioning and sorting tests were conducted using tomatoes and oranges as the test objects. The test results show that the success rate of the target positioning is close to 98%, that of the target classification is close to 98% and that of the grasping is close to 95%. Furthermore, the sorting time of a single target object can be as fast as 1 second, which can meet the requirements of automatic sorting for common agricultural products. The automatic sorting device for agricultural products has a simple structure, reliable performance and low costs. The structure and algorithms proposed in this paper are simple, reliable, and highly efficient and thus can easily realize technology transplantation. These relevant methods provide a theoretical reference for the development of an automatic sorting device for agricultural products.

The flexible job-shop scheduling problem (FJSP) is currently one of the most critical issues in process planning and manufacturing. The FJSP is studied with the goal of achieving the shortest makespan. Recently, some intelligent optimization algorithms have been applied to solve FJSP, but the key parameters of intelligent optimization algorithms cannot be dynamically adjusted during the solution process. Thus, the solutions cannot best meet the needs of production. To solve the problems of slow convergence speed and reaching a local optimum with the artificial bee colony (ABC) algorithm, an improved self-learning artificial bee colony algorithm (SLABC) based on reinforcement learning (RL) is proposed. In the SLABC algorithm, the number of updated dimensions of the ABC algorithm can be intelligently selected according to the RL algorithm, which improves the convergence speed and accuracy. In addition, a self-learning model of the SLABC algorithm is constructed and analyzed using Q-learning as the learning method of the algorithm, and the state determination and reward methods of the RL algorithm are designed and included in the environment of the artificial bee colony algorithm.Finally, this article verifies that SLABC has excellent convergence speed and accuracy in solving FJSP through Brandimarte instances. K E Y W O R D Sartificial bee colony, flexible job-shop scheduling problem, reinforcement learning, self-learning artificial bee colony INTRODUCTIONThe flexible job-shop scheduling problem (FJSP) is an extension of the classic job-shop scheduling problem, and it is a complex combinatorial optimization problem. 1 The FJSP has been a research hotspot over the years. In recent years, artificial intelligence optimization algorithms such as the ant colony optimization algorithm (ACO), 2,3 genetic algorithm (GA), 4-6 bee colony algorithm, [7][8][9][10][11] and various hybrid algorithms [12][13][14][15] have been usedto solve this problem, and some progress has been achieved, but a set of completely good solutions has not yet been reached; therefore, there is room for further research on this problem.Job-shop scheduling is a processing resource allocation problem. It reasonably arranges production resources, processing time, processing sequence, and so on, according to existing constraints to obtain the optimal cost or efficiency. 16 Due to the NP-hard characteristics of the FJSP, it is difficult to achieve global optimization even for small problems. Therefore, many researchers have begun to develop more effective solutions to obtain near-optimal solutions. Because of this trend, to solve the combinatorial optimization problem, many optimization algorithms have been developed. Wang et al. 17 proposed a random weighted hybrid particle swarm optimization algorithm (PSO) based on the second-order oscillation,

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