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)
30 Jun 2026 (Vol - 57 , Issue- 07 )
31 Jul 2026 (Vol - 57 , Issue 07 )
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
A Structural Equation Model Study of the Factors Affecting the Technical and Economic Effects of Rice Mechanization in China
This research establishes a structural equation model (SEM) in order to investigate the factors affecting the technical and economic effects of rice mechanization (EE) and their relationships. Noticeably, six latent variables, including the EE, labor factors, land factors, agricultural mechanization development level (AML), policy and socio-economic condition (SEC), are considered. More-over, the correlations and laws of the aforementioned potential influencing factors are demonstrated for strengthening EE. The findings indicate that the AML is the most sensitive variable in the EE. It is noteworthy that the land factor has not only an effect on the EE directly but also has an indirect influence by the AML however, the total effect of the land is negative. In addition, all of Policy, SEC, and labor are the indirect influencing factors through AML, which plays a completely mediating effect. Therefore, the Chinese government should continuously enhance agricultural mechanization to encompass the entire process with a high level of quality and efficiency. Meanwhile, land and other factors of production such as agricultural technology are fostered to create a mutual adaptation effect.
Development of a monitoring system for grain loss of paddy rice based on a Logistic Model Tree algorithm
China has the world’s largest planting area of paddy rice, and its production of paddy rice is also the first in the world. In China, large quantities of paddy rice fall to the ground and are lost during harvesting with a combine harvester. Decreasing this grain loss is an effective way to increase production and revenue, and scientific studies should focus on this decrease. In this paper, a monitoring system was developed to monitor the grain loss of the paddy rice and this approach was tested on the test bench for precision. The development of the monitoring system for grain loss consisted of two stages: the first stage included collecting impact signals using a piezoelectric film; extracting the four features of Root Mean Square, Peak number, Frequency and Amplitude (fundamental component); and identifying the kernel impact signals using the LMT (Logistic Model Tree) algorithm. In the second stage, the precision of the monitoring system was tested for the paddy rice at three different moisture contents (10.4%, 19.6%, and 30.4%) and five different grain/impurity ratios (1/0.5, 1/1, 1/1.5, 1/2, and 1/2.5). According to our results, the highest monitoring accuracy was 96.7% (moisture content 30.8% and grain/impurity ratio 1/1.5), the average accuracy of the monitoring tests was 89.5%, and monitoring of grain/impurity ratios of 1/1.5 (>92%) had higher accuracy than monitoring the other grain/impurity ratios. Monitoring accuracy decreased as impurities increased. The lowest accuracy for grain loss monitoring was obtained when the grain/impurity ratio was 1/2.5, with monitoring accuracies of 83.6%, 76.8% and 77% and moisture contents of 10.4%, 19.6% and 30.4%.
Parameter optimization and experiment of belt clamping cotton-stalk pulling device
To explore the method for pulling cotton whole-stalk and address the problems of high leakage rate and low pull-out rate, a belt clamping cotton-stalk pulling device was designed. The device is mainly composed of a front suspension device, a pulling and conveying device, a hydraulic control system and dividers. The pulling process of the belt clamping cotton-stalk pulling device was analysed, and the key factors affecting the performance of the device were determined. The three-factor, three-level quadratic regression orthogonal experiment was carried out. Then, the verification test was carried out with the optimized parameters. The results showed that when the pulling height was 61.4 mm, the forward speed was 2.2 km/h, and the driving wheel speed was 244.7 r/min, the average cotton stalk breaking rate was 5.42%, and the average cotton stalk leakage rate was 6.33%, the relative error between the experimental verifivation value and the theoretical optimized value was less than 5%. This study enriches the cotton stalk pulling technology and provides a reference for the development of cotton stalk pulling equipment.
ACCURATE MEASUREMENT OF MAIZE KERNELS COMPONENTS DENSITY BASED ON GRINDING PIECE
To guide the formation mechanism of dry cracks inside maize kernels, a method is proposed to accurately measure the density of maize kernel components. Firstly, the maize kernels are grinded in layers by a layering grinding device. Then, after each grinding, the images of the grinded surface and side of the maize kernels are collected, and the part of the maize kernels removed by each grinding is regarded as the grinding piece, and the mass of the maize kernel grinding piece is measured; Secondly, the images of the down side of grinding piece are segmented by a K-Means clustering mean algorithm into 3 parts——keratinous endosperm, the farinaceous endosperm and the embryonic part. Next, the actual areas of the three parts and the height of the grinding pieces are measured, and we obtain the volume of each components; Finally, the density of each maize kernels components is obtained by a linear neural network model, and a test is validated by the quality of the maize kernels grinding pieces. The test results show that the accuracy of the measurement method reaches more than 96%, which can accurately measure the density of the internal components of different varieties of maize kernels, and provide the basic theory for the formation mechanism of dry cracks inside maize kernels.
The Effect of Design Parameters on the Cost Function of a Worm-helical Gearbox
In this study, a method based on the results of an optimization study on the influence of the main design parameters on the total cost of a worm-helical gearbox was proposed. Specifically, ten main design parameters were investigated to find their influence on the optimum gear ratio of the worm set u1. To do that, a simulation experiment was designed and implemented by a computer program. The results showed that in addition to the influence of the input parameters, their interactions also have important effects on the response u1. Finally, a regression model is proposed and its reliability is confirmed through the results obtained.