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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
16 Aug 2022 (Vol - 53 , Issue- 08 )
Upcoming Publication
31 Aug 2022 (Vol - 53 , Issue 08 )

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 Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
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

Formulation and Synthesis of Pyro-Oil Derived Insect Repellent

Paper ID- AMA-23-11-2021-10874

Despite the abundance of palm-based residues produced, by-products of thermochemical processing, such as bio-oil, may create value-added products to the palm industry. The palm-based derived from bio-oil contains high concentration of aromatic compounds. This study aims to assess the formulations of major insect repellent ingredients (bio-oil as carrier for inert ingredient as well as active ingredient (AI) from lemongrass oil), to test and recommend the most effective insect repellents formulation for the mosquito species studied. Five different cream formulations were created and tested, each with a different bio-oil and AI from lemongrass extract. Based on five different cream formulations, Set C demonstrated the most effective repellency with an equal ratio of bio-oil and lemongrass extract. Set C takes the longest time to repel mosquitoes from white mice, taking 20-21 minutes at a composition ratio of 2:2 to repel mosquitoes. A few tweaks can be made to improve insect repellency efficiency, such as varying the concentration and type of active ingredient and the number of mosquitoes sampled for experiment. Hence, this study adds to our understanding of palm-based residues management towards better environment.

Prediction of Soluble Solids Content of Jackfruit from Skin Surface Using Spectroscopic Method

Paper ID- AMA-23-11-2021-10873

Soluble solids content (SSC) of a jackfruit is a critical quality indicator to evaluate the ripeness of the fruit. To date, there is no portable and low-cost device is available to be used at a field for a rapid maturity screening of a jackfruit. The purpose of this study was to investigate the feasibility of utilizing a shortwave near infrared (SWNIR) spectroscopy to predict SSC of a jackfruit from its skin surface. In this study, 29 fresh jackfruit samples were used. The jackfruits were divided into five main sections from the stalk to bottom to represent different areas of the fruits (top, upper middle, middle, lower middle, and bottom). Then, each section was further divided into six portions, producing 870 skin portions altogether. The spectral data was obtained from these 870 skin portions using a SWNIR spectroscopy. The SSC for each portion was determined using a handheld digital refractometer. A correlation between the spectral data and SSC was developed using a partial least square (PLS) regression method. For the calibration model, the value of coefficient of determination (R2) and root mean square of calibration (RMSEC) were 0.94 and 0.50, respectively. While for the prediction model, the value of R2 and root means square of prediction (RMSEP) were 0.93 and 0.50, respectively. The results indicate that the spectral data correlated well with SSC values. Thus, it is concluded that the SWNIR spectroscopy has the ability to estimate SSC of the jackfruits from their outer skin surface.

Some Physical and Mechanical Properties of Pineapple Leaves from Different Varieties

Paper ID- AMA-23-11-2021-10872

This study aimed to determine some physical and mechanical properties of the common varieties of pineapple leaves in Malaysia such as MD2 (V1), Josapine (V2) and Nanas Madu Kaca A11 (V3). These properties are important to help designers to further apply in chopping/cutting or threshing machine. The physical properties for each variety were measured for 10 different leaves which were collected from different plants. The results indicated that the average length and width of MD2, Josapine and Nanas Madu Kaca were within similar range of 818- 686 mm and 56-58 mm, respectively. The average moisture content was measured for the varieties prior measuring the mechanical properties. The tensile strength for V1, V2 and V3 at different moisture content of 86%, 76% and 83% was observed to be 12.23, 9.16 and 6.45 MPa, respectively. Tensile strength for pineapple leaves is generally decreased with increasing moisture content. For penetration test, the point of hardness (in mm) was measured at the top, middle and bottom leaves. For V1, V2 and V3 the hardness penetration was 2161,2803,2004 mm; 2814, 2364, 3057 mm; 3924, 6630, 3061 mm for each top, middle and bottom, respectively. Whereas for the compression test, the hardness penetration for each V1, V2 and V3 were 1450,1142,14753 mm; 6589,896 and 1734 mm; 16009, 880, 13570 mm for top, middle, bottom, respectively. With respect to the findings of the present research study, it is concluded that different varieties of pineapple leaves exhibit different mechanical properties. Further investigation is required prior application for the cutting ability of the blade in the cutting and chopping machine.

Physical and Mechanical Properties of Sterilized Oil Palm Fruits at Different Component

Paper ID- AMA-23-11-2021-10871

The aim of current work is to investigate the physical and mechanical properties of sterilized oil palm fruit (Dura variety) and its component (kernel and nut) prior to mesocarp and nut separator machine development. The fresh fruit bunch (FFB) samples were sterilized at 100 °C for 140 minutes at 1 atm by using a continuous sterilizer and oil palm fruits were stripped from the bunch manually. Physical properties included the length, width, thickness, mean diameter (geometric and arithmetic), gravimetric properties, and frictional properties were measured. Standard methods were applied in determining the physical properties of the sample. The mechanical properties evaluated were rupture force, deformation at rupture point, hardness and energy. The mechanical properties parameters were determined using Universal Testing Machine. The overall findings show that the average length, width, and thickness of the palm fruitlet were the highest compared to nut and kernel which were 37.28, 25.06 and 21.64 mm, respectively. The shape of the kernel is closer to a sphere compared to the whole fruit and nut as the sphericity value obtained was 79.25. True densities of whole fruits, nuts and kernels were 1079.77, 1140.86 and 1284.76 kg/m3, bulk densities were 767.37, 536.77 and 515.72 kg/m3 whereas the porosity were 20.27, 51.29 and 52.39, respectively. Rupture force, deformation and hardness of sterilized whole fruit, nut and kernel were 878.23, 712.51 and 339.07 N, and 4.86, 3.70 and 2.40 mm, 180.71, 192.57 and 141.27 N.mm, respectively. Overall, the collected data are beneficial for the design of machinery related to oil palm fruit processing such as mesocarp and nut separator machine.

Self-Attention Based Multiscale Learnable Network for Single Image Super-Resolution

Paper ID- AMA-23-11-2021-10870

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.