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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
01 Feb 2022 (Vol - 53 , Issue- 02 )
Upcoming Publication
31 Jan 2022 (Vol - 53 , Issue 01 )

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
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

Mitigation of water stress in Eucalyptus tereticornis sm. using brassinosteroids phyto-hormones

Paper ID- AMA-10-12-2021-10940

Mitigation of water stress by the application of brassinosteroids (BRs) on seedlings of Eucalyptus tereticornis was undertaken in the screen house at CCS Haryana Agricultural University, Hisar. BRs are polyhydroxylated steroidal phyto-hormones, which play an important role in regulation of various plant growth and developmental processes. Keeping in view the future prospects of Eucalyptus tereticornis in agroforestry areas, the present study was carried out on the application of brassinosteroids for raising healthy seedlings for the purpose of plantations on private and public lands. At moisture level of field capacity, growth of plants was found maximum and it decreased as the water stress increased. Foliar application of 5 ppm of BRs at 75% moisture of field capacity, 10 ppm at 60% of field capacity and 20 ppm at 60% and 40% moisture levels were found most suitable to mitigate the water stress in Eucalyptus tereticornis.

Dynamic model and visualization of Arabidopsis thaliana phenotype

Paper ID- AMA-09-12-2021-10939

As an important research subject in precision crop production and agroforestry, an optimized plant phenotype is a prerequisite for plant cultivation management and plant line design. Phenotype development and optimization is also a promising approach to adapt plant cultivation to future climate changes. To analyze the effects of biotic and abiotic stresses on plant phenotypes more accurately, A. thaliana was used as the research object in the early stage of this study. Numerous experimental observations and data analyses were carried out to create a dataset for the virtual growth model of A. thaliana that was simulated using by applying the logistic model. Based on the morphological model of A. thaliana, a dynamic model and visualization were created in this study. The leaf morphological structure model of A. thaliana was developed using the NURBS surface, and its stem model was constructed using OpenGL, which enabled us to construct the main organs model of A. thaliana. Moreover, a leaf editor was developed that could edit leaf traits at different growth periods. Finally, a visual simulation of the development process of A. thaliana from the seedling to maturity under different growth conditions was created, providing an effective approach for studying the effects of environmental stress factors on A. thaliana phenotypes. Our work can provide a theoretical framework for research on a virtual dynamic growth model system of general agricultural and forestry plants.

An integrated framework for apple recognition and localization using deep learning and machine vision

Paper ID- AMA-09-12-2021-10936

With the wide application of Internet of Things technologies, the apple harvesting method is transformed from manual to intelligent and automatic mode. The accurate apple recognition and localization in the orchard is the basic step for the automatic picking. However, most of the existing research focused on the one part of the detection system or just detection of apples in the orchard, which often lead to the not applicable in real-life environment or long computation time of detection. To meet these issue, an integrated framework for apple recognition and localization using deep learning and machine vision is proposed. Firstly, the two sided stereovision camera is configured to establish an automatic apple recognition and localization system. Secondly, the Yolo v3 algorithm is applied to fulfil the apple recognition target. Then, the three dimensional (3D) model reconstruction technology is used to achieve the apple localization goal. At last, a case from our lab is used to demonstrate the feasibility and effectiveness of the proposed method.

The Pivotal Role of Home Environment in Academically Bright Rural Young Adolescents’ Creative Endeavors

Paper ID- AMA-08-12-2021-10935

During the recent few decades educators and parents have emphasized on the special talents, needs and abilities of children. Therefore, solving problems in creative way is recognized as a major factor in achieving high educational aims which is only possible through creativity. Additionally, world requires innovators to solve global social, economic and environmental issues. Hence, both creativity and innovation have become areas of concern worldwide. Hence, the present study was designed in such a way to investigate the influence of home-environment related variables (number of siblings, birth order and land holding) on the young adolescents’ creativity. To achieve this aim, standardized Passi Test of Creativity (PTC, 2006) developed by B.K. Passi was implemented on 300 academically bright rural young adolescents (both boys and girls) aged between 12 and 14 years. Results findings elucidated significant differences in unusual uses flexibility across birth order of the adolescents. Unusual uses fluency, unusual uses flexibility and unusual uses creativity had significant differences across number of siblings. Highly significant differences were elucidated in young adolescents’ inquisitiveness level.

Development of Image Analysis method for physical characterisation and determination of fibre content by physicochemical analysis of banana pseudostem

Paper ID- AMA-08-12-2021-10934

Banana pseudostem varieties from triplod cultivars of Musa acuminata (A) and Musa balbisiana (B) were evaluated in this study for fibre content through image analysis techniques. The samples were dried at 600C in tray drier and were subjected to proximate analysis. At the moisture content of 95.60,94.65,93.20,93.20 and 97.40% for five different cultivars namely Poovan (AAB), Neypoovan (AAB), Rasthali (AAB), Karpuravalli (ABB) and Monthan (ABB) proximate analysis showed Monthan has the highest fibre content (38.4±0.32) and lowest fat content (3.5±0.02). Morphological analysis including size and colour was performed through image processing using LabView software and validated with standard measurements(p≤0.05). Acquired images were subjected to histogram representation through R, G and B components showed a wide range from 5 to 255 for different varieties. The results indicated that colour of Monthan variety (ΔE 57.88±0.3) is lowest compared to other varieties and hence more suitable for development of fibre enriched food products. Image processing was used to measure the external and internal properties of intact fibre in the banana pseudostem for its effective quality grading.