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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
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

Analyze the influence of internal damage of maize seeds on germination based on Micro-CT image technology

Paper ID- AMA-25-11-2021-10897

The germination and growth of maize seeds are directly related to the damage inside the seeds. This paper discussed the relationship between the internal damage of the maize seed after compression and the seed germination rate. Using Micro-CT technology to analyze the characterization of the internal mechanical damage of the seeds. Distinguished the internal tissues and crack damage according to the difference of grayscale value. By counting the number of pixels in different grayscale value intervals, the proportion of each tissue of the seed in the slice layer was calculated. The degree of internal damage was analyzed. After cultivating the tested seeds, the germination results were compared with internal damage to verify the relationship between the germination rate of seeds and internal damage. The results show that the average crack damage of seeds after being subjected to loads of 250 N, 300 N, 350 N, and 400 N are 2.87%, 3.07%, 4.31%, and 4.58%, respectively. At this time, the germination rates of the seeds are 95%, 90%, 25%, and 10%. According to the results, Micro-CT technology can be used to analyze the internal damage of maize, revealing the influence of the internal damage of maize seeds on germination.

Energy Use, Efficiency, and Distribution in Malaysian Oil Palm Cultivation

Paper ID- AMA-24-11-2021-10896

Effective energy management is critical for oil palm cultivation in long- term productivity and profitability. It is imperative to perform investigations into where, when, why and how the energy is being used in the oil palm cultivation activity. This study presents energy use analyses in oil palm nursery and field cultivation in Malaysia. The data of energy inputs use in the oil palm cultivation were retrieved using a combination of actual field measurements and secondary source documents from relevant reputable publications. Annual total energy inputs for a complete oil palm field cultivation was estimated to be 1118.34 MJ/palm/year. Oil palm field cultivation takes the largest share with 99.27% (1110.18 MJ/palm) of the total energy input while oil palm nursery cultivation was 0.73% (8.16 MJ/seed). Machinery utilization is the primary source of energy input in oil palm nursery and field cultivation, accounting for 90.29 % and 96.08% of the total energy inputs, respectively. The energy efficiency of the oil palm cultivation is 4.38, which is considered as an energy-efficient crop cultivation since the output-input ratio is larger than one.

Energy Use, Field Performance and Greenhouse Gas Emission Evaluations of Pesticide Spraying Operations inW etland Rice Cultivation in Malaysia

Paper ID- AMA-24-11-2021-10895

Pesticide application conducted to offer the needed protection to rice plants against weed, disease, insect, and pest infestations. The aim of this study is to investigate the field performance, field time distribution, energy expenditure, mechanization Index, and greenhouse gas emissions from chemicals spraying operation. This conducted study revealed that the wetland paddy fields under transplanting method show 16.3% higher mean effective field capacity, 0.7% lower mean field efficiency, 4.1% lower mean fuel consumption, and 9.5% higher mean operation speed than the fields under broadcast seeding method. The time-motion analysis showed that the laborers spent only 69% of their total working time in the actual spraying task on the crops while the balance 31% of the total operation time was used in filling and mixing pesticides and water in the sprayer tank. The highest contributor to energy expenditure was pesticide energy where it represented 83% to 84.5% of the total energy followed by fuel energy with 14.8% to 13.4%. The total human energy obtained through the conventional method was 34.7% higher mean than the physical human energy recorded using the Garmin method. The heart rate of the worker in performing the spraying operation was the highest compared to the heart rate of the worker in performing the other operations. The total GHG emissions were 66.3 to 92.4 kg CO2eq/ha and chemicals pesticides represent the highest contributor it represented 78.9% to 82.7% of the total GHG emissions. The average mechanization index of the operations in wetland rice cultivation in Malaysia was 70.4%. Fertilizing, planting (broadcasting), and chemicals spraying operation have the lowest mechanization index in wetland rice cultivation.

Geotagged Application for Durian Trees using Aerial Imagery and Vegetation Indices Algorithm

Paper ID- AMA-24-11-2021-10894

Durian demand has increased considerably, and it has gained popularity in the market. Under Industrial Revolution 4.0, precision agriculture is expanding globally with a wide range of digital technologies that provide the farming industry with information to improve farm productivity. The objectives of this study are to geotag the durian trees and to compare several Vegetation Indices (VIs) algorithms (Visible-Band Difference Vegetation Index (VDVI), Visible Atmospherically Resistant Index (VARI), Normalized Green-Red Difference Index (NGRDI), Red-Green Ratio Index (RGRI), Modified Green-Red Vegetation Index (MGRVI), Excess Green Index (ExG), Color Index of Vegetation (CIVE), and Vegetativen (VEG)). One hundred sixty durian trees at the Durian Valley in Kluang (Johor), were tagged, which consist of four sample trees for each treatment. Every two weeks of ground data such as the height of trees, canopy width, girth’s diameter, node distance, pH value, moisture content, electrical conductivity (EC) reading, and leaf sizes were exported into the QGIS software and joined with the tagged durian trees. The aerial imagery data captured the durian plantation area using Red Green Blue (RGB) sensor with a 100 m flight attitude. pH, EC, and moisture content were interpolated using Inverse Distance Weighted (IDW) technique. The processed image by VIs and geotagged trees could help farmers to identify the problem areas in the farm and monitor durian plantation effectively.

Study on a Real-Time Plant Detection Companion Computer of an Agriculture and Forestry Surveillance Drone based on Neural Network Approach

Paper ID- AMA-24-11-2021-10893

The paper presents the results of the study of a plant detection program on agriculture and forestry surveillance quadcopter companion computers. The plant detection program uses an optimized convolution neural network to process the drone camera input video frame by frame and can process up to 38 FPS on the companion computer. The inference speeds up efficiently compared to the original SSD Mobilenet Lite V2 reach approximately 304 times. This performance is satisfied by most real-time applications for agriculture and forestry monitoring flight missions. The network was integrated on a NDIVIA Jetson Nano embedded computer and succeeded in detecting “coconut tree” in different simulation scenarios of a drone flight in real-time. The results demonstrate that the proposed approach could be used for further development of a fully plant detection system using only cameras. They also showed that a good outcome is achievable needing only cheap devices and can be implemented easily on forestry monitoring drones or agricultural drones which are familiar nowadays in Vietnam.