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
Optimal Treatment Systems for Palm Oil Mill Effluent (POME) using Optimization Modelling
Proper treatment system for palm oil mill effluent (POME) is crucial to protect waterways systems and enhance sustainability. Conversion of POME into electricity can utilize POME and generate economic return to the palm oil mill. Optimization modelling can demonstrate optimal operation processes and estimate potential economic return. The objective of this study is to analyze the potential treatment system of POME using a mixed-integer programming (MILP) model. The developed model considered multiple factors such as capital cost, maintenance cost, technology capacity, conversion ratio, and electricity price. General Algebraic Modelling System (GAMS) software version 26.1.0 major release was selected to optimize the developed MILP model. Based on case study, the developed model selects the anaerobic lagoon system (ALS) with biogas and the anaerobic digester (AD) as the most profitable treatment system for POME. The profit gained from the selected system was RM 2,153 900.00. The payback period for the investment was about 9.69 years with a return of investment (ROI) value of 97.71%.
Pineapple Flowering of cv. Pada and cv. Sarawak: Naturally, and Artificially Using Flowering Hormones
Pineapple (Ananas comosus L. Merr) flowering could be induced either naturally or artificially using flowering hormones. Although the latter was widely practiced among pineapple farmers, obtaining optimum flowering of 90% is still challenging to achieve. This issue affected the production cost and would influence the preference of cultivars to be planted. This study aims to determine the period of naturally induced flowering (NIF) and evaluate the efficacy of artificially induced flowering (AIF) and optimize the AIF using flowering hormones at various concentrations on cv. Pada and cv. Sarawak. Both cultivars were grown in polybag under Sarawak, Malaysia’s growing condition. The average NIF of cv. Pada (planted in January 2020), and cv. Sarawak (planted in February 2020), recorded until 470 days after planting (DAP), occurred at 450 and 412 DAP, respectively. The estimated initiation period of NIF on both cv. Pada, and cv. Sarawak occurred during February 2021, which corresponded to the presence of environmental stimuli (low night temperature and water stress). In AIF, optimum flowering in cv. Pada was obtained under ethephon and calcium carbide treatments, but only ethephon treatments work best for cv. Sarawak, despite failing to achieve the optimum flowering regardless of concentrations used. Following that experiment, optimization of ethephon for cv. Pada found that as low as 50 ppm could induce 90% flowering, while lower than that (25 ppm) needs twice application to promote full flowering. Meanwhile, increasing the hormone concentrations in cv. Sarawak still failed to achieve optimum flowering; however, twice application of low calcium carbide concentration (0.5%) could trigger up to 91% flowering. This study suggests that the longer or frequent hormone exposure to the plant was more effective for AIF rather than using unnecessary excessive concentration.
Preliminary Study on Development of Autonomous Robot for Oil Palm Loose Fruits Collector
Oil palm industry is a major contributor to nation’s revenue, however the technology used is simply archaic. Not only that, other industries which are also equally as consequential to generate revenue for the nation have embarked on developing autonomous machines and robot. For this reason, a robot which is entrusted to avail human in amassing loose oil palm fruit has been developed for preliminary studies purpose. While it is a simple robot doing simple tasks in a human-understandable form, it enables straightforward transformation and a revolutionary breakthrough for agriculture industry. This approach imposes the concept of image processing. To narrow the scope of research in this paper, three of the most elementary robot skills: sensing, moving, and vacuuming, are being focused on. To enable future automatic translation from designation to code, a formal designation of the introduced concept has been developed. The robot that has been designed is constructed using cost- efficient materials and works flawlessly under desirable condition. Proximity sensor, as the name implies, is used to measure the distance between the robot and the incoming fruit that is to be sucked. Pi camera, a special camera which is using a proprietary connection with the microcontroller functions as to detect the fruit and send the information to the Raspberry Pi. The probabilities of deploying this robot in the oil plantation are assessed.
A Computer Vision System to Classify Types of Local Indonesian Coffee Beans Based on Convolutional Neural Network
Indonesian local coffee, widely known in the global market, is vulnerable to being counterfeited with other cheaper coffee products. Therefore, we need technology to identify the types of local Indonesian coffee. One of the non-destructive methods for identifying coffee products is computer vision. This study aimed to develop a computer vision method to classify three types of local Indonesian Arabica coffee beans. Those are Gayo Aceh, Kintamani Bali, and Toraja Tongkonan using three types of pre- trained convolutional neural network (CNN), namely GoogLeNet, SqueezeNet, and Inception-v3. Sensitivity analysis was carried out by varying the optimizer, i.e. SGDm, Adam, and RMSProp, and varying the learning rate, which included 0.00005 and 0.0001. Each type of coffee bean used 500 image data for training and validation with a ratio of 70% and 30% and 100 image data for testing. The results show that GoogLeNet, SqueezeNet, and Inception-v3 can achieve up to 100% in validation accuracy value and up to 99.67% in testing accuracy. From the results of this study, the pre-trained CNN model, SqueezeNet with an SGDm optimizer and a learning rate of 0.00005, is highly recommended to classify local Indonesian coffee beans. This is because it has the highest validation accuracy, the highest testing accuracy, the simplest CNN structure, the fastest training time, and the most stable training and validation charts.
Using Global Positioning Satellite (GPS), Image Thresholding, and Tracking and Monitoring Mobile Application to Encourage Urban Beekeeping
This paper describes Try Gonna Bee, an Android-based mobile app that can be used to aid users who are enthusiastic to start keeping stingless bees (Trigona thorasica and Trigona itama species). Eager but inexperienced beekeepers may mistakenly installed bee hives in inappropriate locations due to the lack of guidance. This results in poor quality honey yield and causes the bee colony to move or die. Also, the hive’s entrance must not be situated under direct sunlight because it may melt the propolis structure and cause heat stress. Novice beekeepers may not understand how frequent harvesting, maintaining and re-queening a hive should be done. The developed app determines whether an area is suitable for a beehive installation by using the Global Positioning Satellite (GPS) information, which retrieves an image of the area through Google Maps API. It calculates the greenery percentage of the location, where higher greenery is more favorable (has better access to vegetation). As the hive’s entrance should not face direct sunlight, and the app uses sensors in the phone (gyroscope, compass, and camera) to capture images from the North, East, South, West, and the luminance detection algorithm calculates the direction with the lowest luminance. The app also has a honey tracker function to remind users of upcoming tasks. The accuracy of the app, the usability and users’ experience when using the app was evaluated in a user study with two stingless bee experts, and thirty non-trained users. Results showed that it could determine the greenery percentage better than humans, and accurately (100%) detect lowest luminance direction, compared to novice users (50%). Most of the respondents were satisfied with the usability and experience of using the application. The app can help cultivate higher interests in individuals to be involved with bee keeping, which indirectly improves food security.