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 (2025)
27 Nov 2025 (Vol - 56 , Issue- 11 )
30 Nov 2025 (Vol - 56 , Issue 11 )
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
Improvement of Spectral and Energy Efficiency of Underwater Channel Communication
Underwater Wireless Sensor Networks (UWSNs) consists of various components like vehicles (both underwater and surface), acoustic sensors etc., which can be classified as static, semi-static and dynamic nodes. These are spread across the water bodies to collect the data and to monitor the movement of vehicles, torpedoes etc. All these nodes form as networks and establishes communication with ground stations. Currently, UWSNs face problems and challenges pertain to limited bandwidth, media access control, high propagation delay, 3D topology, spectrum sensing, resource utilization, routing, and power constraints. This proposal deals with the intelligent spectrum sensing in Underwater Cognitive Sonar Communication Networks (CSCN). Here, the improved performance of spectrum sensing in underwater communication is attained by optimizing the cooperative spectrum sensing and data transmission. The parameters of system like sub-channel allocation, and transmission power are optimized by a new hybrid meta-heuristic algorithm by integrating the concepts of Whale Optimization Algorithm (WOA), and Grey Wolf Optimization (GWO) termed as WGWOA. The main intention of optimizing these parameters is to maximize the Spectrum Efficiency (SE) and Energy Efficiency (EE) of the underwater channel communication system. The analysis is done with respect to convergence rate, minimum detection probability, and local sensing time.
ROBUST HYBRID VISUAL DIGITAL DATA AUTHENTICATION WITH HUMAN VISUAL CHARACTERISTICS
Robust watermarking proposals supported on human visual characteristics with a series of hybrid transform of type discrete wavelet transform (DWT) followed by singular value decomposition (SVD) is wished-for. By analyzing the matrices U or V through SVD, it is bringing into being that there stay alive a well-built relationship amid the internal column elements of U or internal row elements of V. Hence, this work will make the most of these chattels for image watermarking. At the outset, visual digital data is segregated into 8 × 8 non-overlapping pixel blocks and each block is processed for brinks by using the algorithm of detection for a canny brink. An appropriate block is decided to pick in such a way that the number of brinks in each block is only about or equal to a threshold. A threshold is defined by finding the mean of the brinks in each block of the host visual digital data. Using these appropriate blocks, we will form an image of reference. This reference image is processed by a series of operations DWT-SVD. Then, the watermark is implanted by adapting the nth column of the U matrix of the host image with the nth column of the U matrix of the watermark image. The same operation is applied on the V matrix instead of a column vector, use a row vector. The adapted relation is wont to retrieve a watermark. The experimental findings demonstrate that the ideal watermarking algorithm will guarantee that the typical image processing operations and geometric attacks are invisible and more stable. The efficiency of this proposed method is out of shape than other proposed methods examined in this research.
A Deep Learning Technique for Automatic Classification of HSI Land Use and Land Cover images
In the field of remote sensing, Land Use (LU) and Land Cover (LC) classification problem is one of the major formidable tasks. In this paper, we have considered Visakhapatnam City for LU and LC classification. Neural Networks has become a fast enhancing tool in order to accomplish complex tasks in many challenging applications in the field of Artificial Intelligence (AI). Various kinds of Neural Networks are existing nowadays to cater wide range of applications. In this paper, implementation of Convolutional Neural Network (CNN) for a problem is considered. CNN is a kind of Deep Learning technique, which is generally applied to applications related to classification of images, clustering them depending on similarity measure and does object identification within the images. Using CNN, we have performed LU and LC Classification of Visakhapatnam City and attained an accuracy of 95.38%.
Enhancement of Antenna Gain using Partial Removal of Substrate Technique for V2X Applications
This paper presents a CPW-fed antenna with enhanced gain for sub-6GHz band wireless and vehicular safety applications. In the antenna design, partial removal of substrate technique is incorporated to obtain improved gain values and omnidirectional characteristics. Initially, a basic CPW-fed antenna is designed, which operates in the band of frequencies from 3 GHz to 6.41 GHz exhibits poor impedance matching. This antenna radiates lesser gain values at the upper resonating band above 5.6 GHz. Further improvement of broadside gain can be enhanced by a factor of 2.49 dB at 5.9 GHz DSRC band using partial removal of substrate technique. And the antenna impedance matching is greatly improved in the range of frequencies from 2.71 GHz to 7.43 GHz. The measurement results of the fabricated prototype show a good match with simulated results. The antenna is located on the vehicle (car) rooftop in a virtual atmosphere with the help of the ANSYS SAVANT tool and investigated 2D-radiation patterns and 3D-gain plots. The same antenna is located at two positions of the car model and obtained the antenna to antenna coupling factor, which is well below -30 dB.
MILK QUALITY PRESERVATION THROUGH THE APPLICATION OF PLANTARICIN IIA-1A5 AS BIOPRESERVATIVES ON FRESH MILK AT DAIRY COW FARM
Milk is a perishable product. The causes of milk breakdown can be caused by various factors, such as pathogenic bacteria. Plantaricin IIA-1A5 is a bacteriocin, antimicrobial substrate that is produced by Lactobacillus plantarum IIA-1A5, a lactic acid bacteria isolated from fresh beef at traditional market in Indonesia. Plantaricin IIA-1A5 has properties to reduce the number of bacteria contamination in food. The purpose of this study was to determine the effect of crude plantaricin on fresh milk from dairy cows in terms of physicochemical, pH, and microbiological aspects. Three types of treatment on milk consisting of untreated (control), plantaricin 3.66 ml, and synthetic antibiotic penicillin 3.66 ml. The three milks were storage at room temperature at 4-time intervals, 1 h, 3 h, 5 h, and 7 h since treatment was given with 3 repetitions. Physicochemical testing was carried out using lactoscan, pH testing using a pH meter, and total microbes. The results showed that there was no significant difference in the physicochemical properties of milk and pH, but there was a significant difference (P = 0.05) in the S. aureus population at the 6th hour after giving plantaricin treatment. Therefore, it can be concluded that plantaricin has the potential as an alternative preservative for cow milk.