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
Self-Attention Based Multiscale Learnable Network for Single Image Super-Resolution
The convolutional neural network (CNN) has won unprecedented triumph in super-resolution (SR) reconstruction via making full use of the advantages of fast parallel computing, end-to-end high training accuracy. However, the model CNN based can only extract fixed-size local feature information, and has limited ability to obtain global information. Moreover, scale fusion mechanism was rarely employed in model training in the past, and diverse degradation models with different scales need to be trained and tested separately. We put forward a new self-attention based multi-scale learning network (SAMSSR), which can address the above problems. On the one hand, extract the global similarity in degraded images in virtue of self-attention mechanism, and the similarity is acted as the feature weights to redistribute the feature channel information, so as to enhance the important information and suppress the non- important information, finally improve the reconstruction quality and simplify the training process. On the other hand, taking advantage of intrinsic similarity and correlation of SR results with different scales reconstructed by model and deeply merging them, we ultimately achieved multi-scale SR outputs, consequently realized a single end-to-end multi-scale SR reconstruction. Extensive experiments demonstrated that SAMSSR obtained competitive promotion in accuracy and visual perception compared with state- of-the-art methods.
Lightweight Design of Tractor Drive Axle Housing based on Six Sigma Robust Multi-objective Optimization
According to the consideration of safe use, the design of tractor driving axle housing is too conservative, with its strength failing to demonstrate. Aiming at the lightweight design of the axle housing, this paper proposes a design method to ensure the reliability and robustness of the axle housing on the basis of realizing the lightweight. Firstly, based on the analysis of the natural frequency of the axle housing, the finite element model is verified, and the resonance phenomenon is analyzed. Secondly, the optimal area of axle housing is determined by the results of variable density topology optimization and the fatigue accumulation damage theory. In the optimized area, the shell thickness variables are selected by sensitivity analysis. Thirdly, based on the radial basis function (RBF) approximate model, a six-sigma robust optimization algorithm is adopted to optimize the axle housing with mass, equivalent stress, displacement and safety factor as the objective function. The simulation results show that the design variables, strength, stiffness and safety factor of the optimized drive axle housing reach 8σ level, and the mass of the axle housing decreases by 9.7%. Moreover, compared with the deterministic optimization design based on genetic algorithm, the safety factor, strength and stiffness of the bridge housing through the proposed method can be improved relatively while achieving the goal of lightweight, and meanwhile it provides a technical reference for the structural lightweight design of vehicles and other machineries.
Performance Improvement of Fruit Battery-based Sensor with Charging Technique
The grades of oil palm fresh fruit bunches (FFB) influences both processing and market values as this oil palm sector is highly contributed to Malaysia’s Gross Domestic Product (GDP). Traditional techniques used during the screening process must be enhanced in order to minimize the quantity of low-quality oil palm that is delivered to the mills for further processing. This study presents an innovative method to grading the oil palm, based on a fruit-based maturity sensor with charging technique. The sensor's sensitivity is evaluated by examining the relationship between the sensor voltage and the moisture content of the oil palm fruit, which is done with the use of several set of electrodes in this research. When a four-set of electrodes in fruit battery was utilized instead of a single set of electrodes, the sensitivity of the sensor improved by triple times for the range of 50 to 80 per cent moisture content, compared to the single-set of electrodes. The accuracy of fruit maturity grade is the common difficulty encountered throughout the receiving process, was substantially enhanced as a result of the suggested method.
Study on The Mechanical and Control Aspect in the Design of a Robotic Harvesting System for Vertical Farm
According to The United Nation (UN), 80% of the world population are predicted to be living in the Urban area by 2050, food security is going to be a real issue especially in the urban area. Therefore, sustainable agricultural solutions like vertical farming, indoor farming and robotic harvester are to be widely studied and explored in order to ensure the survivability of humankind of the next generation are secure. This paper presents some of the critical parameters needed to design a robotic harvester in a vertical farming greenhouse. The vertical farm in this study has 5 levels with 3 rows on each level and adopting the Nutrient Film Technique (NFT) Hydroponic system. The robotic harvester was proposed to have a robotic arm moving on a vertical and horizontal rail. The robotic control system was design to work wirelessly using Radio Frequency (RF) as the transmitter. Five experiments were done in order to determine some of the critical parameters and the parameters were wireless connectivity, wireless coverage, robot aim calibration, robot arm torque limitation and natural arm movement programming approach.
Soil Compaction Effects of Large and Mini Combine Harvesters in a Paddy Cultivation Area in MADA
The most effective mechanization technique for harvesting yield in rice field cultivation is the use of a combine harvester. A variety of combine harvesters are used in the Muda Agricultural Development Authority (MADA) region, with the majority of them being locally modified large combine harvesters (CB). Concerns about soil compaction (SC) persuade the local government to provide a mini combine harvester (CM) as an alternative machinery. However, the extent of soil damage caused by the varying weight and contact pressure of combine harvesters has not been clearly reported. As a result, the goal of this study was to compare the SC effect of two types of combine harvesters used in the MADA region: CB and CM. The test plots were chosen from a total of 2.6 ha in Tunjang, Wilayah II – Jitra, Kedah, and were planted in silty clay soil texture during both wet and dry seasons. Over 10,000 data points on SC were collected using a soil penetrologger. Normalized SC values shows better approach in identification of drastic change of SC at specific soil depth. The SC also shows no significant different on straight vs. cornering both for CB and CM. At a critical soil depth of >30 cm, it was discovered that the compaction caused by the CB was 7 to 10% greater than that caused by the CM. However, there was no noticeable difference in SC effect at the top soil layer (0-30 cm) from different type of combine harvester. Thus, the long-term impact of CB deployment must be considered due to changes in farming techniques and climate variability, such as soil water content during the monsoon season.