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.
WOS Indexed (2026)
07 May 2026 (Vol - 57 , Issue- 05 )
31 May 2026 (Vol - 57 , Issue 05 )
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
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.
Uneven illumination enhancement method of straw mushroom image in mushroom house
The growth monitoring of edible mushroom based on machine vision is an important part of the production and management of industrial edible mushroom. In order to solve the problem of serious uneven illumination of straw mushroom image in closed and no light straw mushroom room environment, this paper establishes the gray level calculation formula of mushroom bed image under multiple LED light sources, and construct a reasonable mask background image used to enhance the straw mushroom image. The processed image result showed the average, deviation and range of gray scale of mushroom image decreased by 58.2%, 65.81% and 45.28% respectively, the mean, deviation and range of background gray decreased by 97.8 %, 81.69 % and 61.02 % respectively. Compared with Multi-Scale Retinex and Homomorphic Filtering, the algorithm in this paper performs better in concentrating of gray distribution and increasing the image gray level difference of straw mushroom and culture material, which is helpful for the subsequent segmentation and recognition of the mushroom image.
Rice Lodging Analyse Based on Gray Level Co-occurrence Matrix
Rice lodging refers to the phenomenon that rice crops growing upright are skewed and even the whole plant falls to the ground due to the influence of external forces. When the paddy field that has fallen down is harvested mechanically, it is necessary to adjust the harvesting direction, the height of the header and other factors according to the specific lodging situation. In the rice field image taken from the driving perspective of the harvester, the texture feature of the rice field image in the harvest area will rise or fall rapidly and shake greatly due to the lodging of part of the rice. In this paper, the gray level co-occurrence matrix analysis method and MATLAB simulation tools are used to analyze the texture characteristics of the four main factors of Angular Second Moment, Correlation, Inverse Different Moment and Contrast in the image of the area to be harvested from the driving perspective of the harvester. It provides a basis for determining the texture feature parameter criterion when using the neural network to detect the lodging situation of rice.
Design and Analysis of Energy-saving Forward Deep-rotary Tillage Machine
Rotary tillage operation generally has problems such as shallow tillage depth (8-15cm), low depth of mixed burying of straw, and high power consumption. Increasing the tilling depth by increasing the turning radius of the rotary tiller has problems such as huge mechanism and increased power consumption. In this paper, a deep rotary machine was designed, which could still achieve deep operation (tillage depth of 20cm) when the blade turning radius was reduced. Through the method of simulation and experiment, the article compared the operating performance of the deep-rotating machine and the common tool with increasing rotary radius. The result shown the advantages of the deep-rotating machine in the index of tillage depth and power consumption; the parameter pair of the deep-rotating machine was clarified. For the impact of job performance, the methods of variance analysis and response surface analysis were adopted to explore the relationship between parameters and indicators and fitted mathematical models. Field experiments were carried out and compared with the simulation analysis results. The errors between field test results and simulation analysis were 8.2%, 3.5% and 4.2% in power consumption, soil fragmentation rate and straw coverage rate. The average error in soil vertical displacement was 15.2%, and the longitudinal displacement error was 16.9%. The average error in the vertical distribution of straw is 15.7%.
Research on Mask-RCNN-Based Online Monitoring System for Wheat Impurity Rate and Breakage Rate
A sampling device that can collect high-quality images of wheat grain was designed, and the image was processed by Mask-RCNN algorithm. Then the calculation model which can calculate the impurity rate and breakage rate based on image information was established. Wheat grain images collected in the field were labeled and trained. The trained algorithm can identify the number of wheat kernels, the pixel area of damaged wheat kernels, and the pixel area of impurities in the image. Then the three data were input into the calculation model to obtain the impurity rate and damage rate. This protocol enabled the fast recognition and segmentation of wheat grain images with 90.46% recognition accuracy for intact wheat seeds, 88.85% and 90.32% segmentation accuracy for broken wheat seeds and impurities. The relative error of the method for monitoring the impurity rate and breakage rate of wheat is less than or equal to 9.86%. The research in this paper can monitor the wheat impurity rate and breakage rate when the combine harvester is harvesting wheat, which helps the driver adjust the combine harvester in time to improve the harvesting efficiency, and it can provide a reference basis for intelligent regulation of combine harvesters.