Scopus Indexed
WOS Indexed (2022)
clarivate analytics

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.

Submission Deadline
01 Feb 2022 (Vol - 53 , Issue- 02 )
Upcoming Publication
31 Jan 2022 (Vol - 53 , Issue 01 )

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:

Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science
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

Calibration and Model Optimization of Simulation Contact Parameters of Potassium Fertilizer Particles Based on Discrete Element Method

Paper ID- AMA-07-12-2021-10927

To accurately obtain the contact parameters between the discrete element model of potassium fertilizer particles and improve the efficiency of the simulation test, the contact parameters between potassium fertilizer particles were calibrated and the potassium fertilizer particle model was optimized. Firstly, the nonstandard ball model of potassium fertilizer particles was established in EDEM 2020 software. Secondly, taking the contact parameters between the discrete element model of potassium fertilizer as the experimental factors and the accumulation angle as the response value, the central composite test was designed to obtain the contact parameters of the discrete element model of potassium fertilizer particles. Finally, based on the response surface method of the regression model, the particle size of the potassium fertilizer particle model and the smoothness of the filled particle model were optimized to obtain the optimal discrete element particle model of potassium fertilizer, and the verification tests before and after optimization were carried out. The experimental results show that the relative error between the optimized simulation test and the physical bench test is 0.58%, and the simulation time after optimization is saved by 67.16% compared with that before optimization. The optimized potash particle model improves the simulation efficiency on the premise of ensuring the accuracy of simulation, and the research results can be used as a reference for studying the characteristics of potash particle material by the discrete element method.

A Structural Equation Model Study of the Factors Affecting the Technical and Economic Effects of Rice Mechanization in China

Paper ID- AMA-07-12-2021-10926

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

Paper ID- AMA-07-12-2021-10925

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

Paper ID- AMA-06-12-2021-10924

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.


Paper ID- AMA-05-12-2021-10921

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.