WOS Indexed (2022)
clarivate analytics

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
07 Dec 2022 (Vol - 53 , Issue- 12 )
Upcoming Publication
30 Nov 2022 (Vol - 53 , 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:

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

Effect of Seed Priming and Plant Geometry on Growth and Yield of Wheat in Modified System of Wheat Intensification Under Irrigated Sub Tropics of Jammu

Paper ID- AMA-13-08-2021-10617

A two year field experiment was conducted to investigate the effect of seed priming and plant geometry on growth and yield of wheat crop in modified system of wheat intensification under irrigated sub tropics of Jammu. Experiment was laid out in randomized block design with three replications and ten treatments viz.conventional sowing (Check), un-primed seed sown at plant geometry of 20 cm × 5 cm, un-primed seed sown at plant geometry of 20 cm × 10 cm, un-primed seed sown at plant geometry of 20 cm ×15 cm, un-primed seed sown at plant geometry of 20 cm × 20 cm, conventional sowing of primed seed, primed seed sown at plant geometry of 20 cm × 5 cm, primed seed sown at plant geometry of 20 cm × 10 cm, primed seed sown at plant geometry of 20 cm × 15 cm and primed seed sown at plant geometry of 20 cm × 20 cm to assess the effect of seed priming at different geometry on growth and yield of wheat under MSWI. The results indicated that highest growth attributes and grain yield of wheat i.e.5244.7 kgha-1 was recorded in primed seed sown at plant geometry of 20 cm × 5 cm which was statistically at par with primed seed sown at plant geometry of 20 cm × 10 cm and conventional sowing of primed seed. Further, primed seed at different plant geometry also significantly improve growth attributes of wheat as compared to un-primed seed.

Crop Production Forecasting in Turkey by using Time Series and Machine Learning Algorithms

Paper ID- AMA-13-08-2021-10616

The objective of this study is thus to find suitable time series machine learning models of forecasting for cereals, vegetables, fruits, and wheat production in Turkey. Turkey, situated at the crossroads of Asia and Europe, is among the larger countries of the region with a population of over 85 million in terms of territory. Agriculture employs about a quarter of the workforce and generates most of the income and employment in rural areas. We examined five machine learning algorithms, including autoregressive integrated moving average (ARIMA), Prophet, elastic-net regularized generalized linear (GLMNet), random forest, and eXtreme Gradient Boost (XGBoost) using R programming. The performance of the algorithms was evaluated using the mean absolute percent error (MAPE). As a result, the algorithms that give the best estimates based on the MAPE error metrics were found as ARIMA and GLMNet.

Improvement of Spectral and Energy Efficiency of Underwater Channel Communication

Paper ID- AMA-12-08-2021-10615

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.


Paper ID- AMA-12-08-2021-10614

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

Paper ID- AMA-12-08-2021-10611

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%.