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WOS Indexed (2024)
AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi
18 Apr 2024 (Vol - 55 , Issue- 04 )
30 Apr 2024 (Vol - 55 , Issue 04 )
Aim and Scope :
AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
Agricultural and Biological Sciences
Electrical Engineering and Telecommunication
Electronic Engineering
Computer Science & Engineering
Civil and architectural engineering
Mechanical and Materials Engineering
Transportation Engineering
Industrial Engineering
Industrial and Commercial Design
Information Engineering
Chemical Engineering
Food Engineering
Parallel Delaunay Triangulation for Large Data in Geographic Information System
Every day, data processing becomes increasingly important. It's vital to use high-performance computing to process such big data. There are billions of spatial points in Geographic Information Systems (GIS) to be managed within a reasonable period. One of the basic operations is to prepare triangulation data. This study proposed and implemented methods to produce Parallel Delaunay Triangulation for Large Data in Geographic Information System. Our proposed approach is based on the Divide and Conquer algorithm. The set of points in the regions can be divide into independent partitions, and each partition is separately triangulated. Lastly, we used stitching methods to merge these regions into a single result. In our implementation, we use C++ and MPI to evaluate our algorithm.
Gearbox design and dynamic load analysis of rice tracked combine harvester device
In this paper, aiming at the problems of low reliability and high failure rate of crawler combine gearbox, based on the theoretical design, the dynamic simulation of transmission system is carried out by using ADAMS software. The simulation results show that the transmission system is similar to the theoretical design results, and the fluctuation of the angular velocity curve of each axis is small, which preliminarily verified the transmission stability and accuracy of the gearbox. Then, transient dynamics analysis and modal analysis of critical components were performed by ANSYS simulation software. The results show that the strength of the critical parts meets the requirements and the critical parts do not resonate. Finally, the gearbox was prototyped and assembled on the test vehicle. The results show that the gearbox has good straight-line driving and cornering performance. The minimum natural frequency of the axis is 182.49HZ, which is lower than the frequency of its operation and does not cause resonance. The results of this paper will provide a basis for designing a gearbox design and dynamic load analysis of rice tracked combine harvester device.
Method to Recognize Litchi Fruit by Improved YOLOv3
High-accuracy recognition of litchi fruits is the key to yield estimation. To solve the problem of low detection rates of dense, small targets in orchard scenes, this paper proposes an improved YOLOv3 method for litchi fruit recognition. The litchi fruit target anchor frames in the dataset are re-clustered to obtain nine predefined anchor frames. The prediction scale of the network is adjusted, a 160 × 160 feature prediction scale is added to improve the detection of small targets, and the feature prediction scale for large-target detection in YOLOv3 is removed to simplify the network model. A dense connection module is added to the feature-extraction network to enhance the feature propagation capability and improve network performance. To train and test the litchi fruit dataset, immature (young fruit and expansion stage) and mature stages were constructed in an orchard scene, and 227018 labels were generated using LabelImg software. The F1 score and mean accuracy precision (mAP) were used to evaluate the network model, and the proposed method was experimentally compared with YOLOv3 for litchi images. The experiments showed that the recognition effect was significantly improved using the proposed model, with F1 and mAP of 0.851 and 88.9%, respectively, on the full dataset, which are better than YOLOv3 by 0.238 and 31.1 percentage points, respectively. Therefore, the method has a good effect for dense litchi fruit recognition in orchard scenarios and provides technical support for litchi yield estimation.
Design and field experiment of self-propelled Chinese little greens harvester
Aiming at the problems of difficult harvesting, low efficiency and high labor intensity of Chinese little greens, a self-propelled Chinese little greens harvester is designed. The bidirectional electric drive reciprocating cutter device is adopted to effectively improve the cutting efficiency and cutting quality. The motion cutting diagram of double-action cutter under different cutting speed ratio is drawn, which shows that the best cutting speed ratio to reduce the area of heavy cutting area and missing cutting area is k = 1.7, which could effectively reduce the phenomenon of heavy cutting and missing cutting. The inclined vertical flexible and orderly clamping conveying device is adopted to control the conveying speed at 1.2 ~ 1.5 times of the walking speed of machine to ensure the smooth conveying process and avoid congestion. The angle between the conveyor and the horizontal ground is controlled at 10 ° ~ 20 °. The field experiment shows that the performance of the harvest is good. The average time of harvesting Chinese little greens per 100 m2 is 57.17 seconds and the average loss rate is 6.475%. The research results could provide reference and theoretical basis for the development of other leafy vegetable harvesting machinery technology.
Path tracking and obstacle avoidance planning of tracked robot based on model predictive control
In order to ensure the stable and safe driving of tracked robots in complex farmland environment, this paper proposes a path tracking and obstacle avoidance planning method for tracked robots based on model predictive control. According to the kinematics analysis method, the kinematic model in the form of state equation of the tracked robot is established. Considering the influence of the unavoidable system uncertainty and external disturbance on path tracking accuracy, the path tracking control layer based on MPC theory is designed with control input as constraint. Then, the point-mass model is used to describe the movement of the tracked robot in the work area, so as to reduce the amount of calculation in the path re-planning layer. The obstacle avoidance function is selected according to the proportion of penalty function and the velocity of tracked robot. The complete MPC controller for path tracking and obstacle avoidance planning of tracked robot is established through the combination of path tracking control layer and path re-planning layer. The final results and analysis show that the method proposed in this paper can achieve smooth avoidance of obstacles and improve the path tracking control accuracy of tracked robots.