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.
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
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.
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.
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.
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.
Soft brown sugar is a sugar product with a distinctive brown colour due to the presence of molasses, known for its added flavour and faster caramelization properties. It is widely used as an ingredient for baking goods. The problem of caking in soft brown sugar has been causing quality deterioration as well as interfering with manufacturing and handling processes in the industry. The aim of this study is to determine the possible factors leading to the caking of sugar in terms of physical properties and flowability properties. These factors are moisture content that is related to colour absorbance as an indication of molasses content, mean particle size, and flowability properties. The moisture content was found to be approximately proportional to the colour absorbance. The mean particle size and moisture content influenced the flowability of the soft brown sugar samples. Compaction of brown sugar into compacts was conducted to imitate industrial warehouse storage conditions in order to investigate the factors of caking. High moisture and fine mean particle sizes were the factors that caused formation of solid bridges between particles hence caking found in this study. In conclusion, the factors that caused caking were high moisture content and fine particle size of brown sugar samples.