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
Study on the citrus canker (Xanthomonas axonopodis pv. citri) prevalence in different agro-climatic zones of Jammu region and efficacy of chemical, botanicals and biocontrol agents for its management.
To assess the incidence and severity of citrus canker disease in different districts of Jammu region, a comprehensive survey was conducted across five districts viz. Samba, Jammu, Udhampur, Kathua and Reasi. The results revealed regional and seasonal variations with Samba district of the area exhibited the highest mean Percent Disease Index (PDI) of 50.81% during August-September period which gradually decreased across the months. The pathogen of citrus canker was isolated, identified and confirmed through pathogenicity testing. In vitro evaluation revealed that Bacillus subtilis was the most effective bio-control agent, producing an 18 mm inhibition zone, while among botanicals, neem extract showed the highest efficacy with a 15 mm inhibition zone and copper oxychloride was identified as the most potent fungicide, with a 25 mm inhibition zone, while streptomycin sulfate exhibited the strongest antibacterial activity among antibiotics, achieving a 22 mm inhibition zone. These findings underscore the importance of integrated disease management strategies, combining biological, chemical and botanical approaches to effectively control citrus canker and reduce its impact on citrus production in the region.
Health and Sustainable Development
Health and sustainable development have aspects and objectives, as well as indicators for sustainable development in general and health in particular, in order to achieve a set of established goals. The World Health Organization has developed strategies and policies to advance the field of health. In this regard, attention is focused on care and management of diseases to achieve well-being in health. Therefore, health insurance and its services play a significant role in achieving a set of sustainable development goals, impacting the essential aspects that countries are currently striving to achieve while expanding the scope of other accomplishments in the future. The ultimate goal is to develop quality health care that keeps pace with global developments to achieve sustainable development. Arab countries have made significant progress in several fields, focusing on health, and have become aligned with global developments within the objectives and global standards in this area, gradually moving towards achieving similar outcomes as developed countries in the health sector, especially given its critical and essential importance in community life.
Microgrid conception proposal and economical study for PV power plant of Zaouiet Kounta (Adrar)
There have been global initiatives for the promotion of self-sufficient renewable energy systems. This initiative has led to the development of renewable power generating systems which are capable of providing self-sufficient power generation with the use of more than one renewable source of energy. We can assume that the most commonly used hybrid renewable energy sources in Algeria are solar and wind energies. Climate of Algerian Sahara is hot, sunny and arid. This part of Algeria is a hot desert, located on both sides of a tropic. Daytime temperatures are very high, can exceed 50°C, and the thermal amplitude between day and night is often higher than 350 C or 400C. Addition to this, there is many microclimates which are characterized by very high wind speed. It means that both of wind and photovoltaic energies are widely suitable in this area, especially when we assume that distribution of population is much disseminated. Creating some Microgrids for local consumption will be an interesting solution to provide energy to these populations. But the most important challenge is to provide the best design and management of these systems regarding many parameters such as the location, type of loads, and energy produced cost.
Heterogeneous machine learning curve fitting for accurate fault location in VSC HVDC bipolar system
Exploring advanced diagnostic methods for High Voltage Direct Current (HVDC) systems is essential to swiftly identify and address faults. A robust fault location system that can accurately pinpoint issues is crucial for minimizing downtime and improving overall system reliability. To achieve this, we propose a novel approach which involves leveraging a heterogeneous combination of machine learning and curve-fitting methods. This innovative method addresses the complexity and non-linearity of fault location and detection by utilizing machine learning algorithms to identify patterns and anomalies, while curve-fitting techniques, along with trust region optimization, refine these predictions to enhance precision. The synergy between these techniques not only improves fault location accuracy but also ensures rapid response times, thereby significantly enhancing the reliability and efficiency of HVDC systems. Results from the comparative analysis demonstrate that the proposed method, using peak current signals sampled at 135 kHz, achieves the lowest error rate max average error MAE of 0.316% , and coefficient of determination of 0.99 when the range of maximum average error in existing fault location methods is between 0.78% and 5.69%.
A Trend-Based Approach for Day-Ahead Electricity Price Forecasting and Peak/Off-Peak Hours estimation in the Local Electricity Market
Electricity prices forecasting (EPF) has gained significant attention due to its relevance in various domains, such as optimizing energy management, electricity contract pricing, demand management, and informed decision-making. In this study, We have investigated the implementation of an EFP method in the local electricity market involving prosumers and suppliers, To achieve this, we propose a novel trend based approach that combines Bidirectional Long Short-Term Memory (BI-LSTM) for day-ahead electricity price forecasting with the k-means method for identifying off-peak and peak hours. To validate our methodology, we attempted to approximate the electricity prices of the local electricity market in Germany by introducing a variation factor that allowed us to consider additional costs added to the wholesale market price (such as taxes, supplier profits, etc.). we used real data from both "ENTSO_E transparency platform" and “EUROSTAT”, we also conducted extensive tests from 2019 to 2023. the model was trained using different subsets of data, and prediction time length. We also explored two clustering approaches with two k-mean configuration using a series of 24 and 12 hour, distinguishing between daytime and nighttime periods. This trend based approach enabled us to show that we can achieve a reasonable estimation of peak and off-peak hours,, with an average accuracy of 20 out of 24 correct clustered hours over a 5-year testing period, Despite the deviation observed between the actual and predicted values by BI-LSTM. However, we noticed that the increase in renewable energy sources, particularly solar and onshore wind, impacted the model's outcomes in 2020, especially in daytime, But in subsequent years such as 2022 and 2023, after retraining the model with data that accounts for the increasing influence of renewable energies, we observed improved accuracy and adaptability. The model became more capable of estimating Peak/Off-Peak Hours, even amidst the changing dynamics introduced by the intermittent nature of solar and onshore wind energy sources.