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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. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology Research Journal of Chemistry and Environment Consultant (ISSN:0010-7069) Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/ Journal Of Transportation Systems Engineering And Information Technology

Submission Deadline
18 Jul 2024 (Vol - 55 , Issue- 07 )
Upcoming Publication
31 Jul 2024 (Vol - 55 , Issue 07 )

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

Economics of Mango Production and Constraints Faced by Growers in Western Undulating Agro Climatic Zone of Odisha

Paper ID- AMA-21-03-2023-12140

Mango is an important commercially farmed fruit crop and it is very popular due to its wide range of adaptability and high nutritive content. In order to understand the economics of mango production and the challenges faced by growers in production, the current study was carried out in the Kalahandi area of Odisha state of India. The study used a multi-stage sampling design with a 60-person sample size. Its information was gathered for 2021-22. Use a straightforward cost-return calculation to calculate the cost and profit of mango farming. The growers' challenges with mango production and marketing were ranked using the percentage method. Mango cultivation costs per quintal were calculated to be Rs. 1167.43, while per-hectare gross returns were, on average, Rs. 303944.67. The overall output-input ratio was 2.14, which is greater than unity and showed that mango production was a successful business. Lack of knowledge about high-yielding mango varieties, the issue of high temperatures, the problem of unavailability of transportation in sick, heavy rains, winds, and hailstones during flowering and fruit development stages, lack of extension services, and a lack of storage facilities near the growing area were among the production and marketing challenges faced by mango growers. The findings are in line with those of Yadav et al. (2010) who identified fruit drop as one of the major restrictions on mango output.

Effect of irrigation management on growth, yield, water use efficiency and economics of fennel (Foeniculum vulgare Mill)

Paper ID- AMA-19-03-2023-12136

A field trial was carried out at SKN College of Agriculture, Jobner, Jaipur (Rajasthan) during rabi 2018-19, 2019-20 and 2020-21to find out the effect of different irrigation methods (flood irrigation, sprinkler irrigation and drip irrigation) on growth, yield, water use efficiency and economics of fennel (Foeniculum vulgare Mill.). The experiment comprising of five treatments viz. flood irrigation, sprinkler irrigation, drip irrigation at 0.8 IW/CPE ratio, drip irrigation at 1.0 IW/CPE ratio and drip irrigation at 1.2 IW/CPE ratio. Research findings of present experiment revealed that the drip irrigation treatments brought an additive effect in increasing growth, yield, water use efficiency, quality and economics of fennel. Drip irrigation at 0.8 IW/CPE ratio produced higher plant height (105.33 cm), umbels/plant (27.44), umbellets /umbel (22.40), seeds/umbel (330.73), seed yield (25.56 q/ha), harvest index (30.61%), essential oil (1.70 %), net returns (Rs 135720/ha), B:C ratio (3.43) and water use efficiency (7.22 kg/ha-mm) along with 33.03% of water saving which was significantly superior in the comparison of flood irrigation and sprinkler irrigation.

Genetic Parameters and Characterization of Rice Genotypes under Drought Stress

Paper ID- AMA-17-03-2023-12132

Two experiments were conducted to evaluate seventeen rice genotypes behavior under normal and water deficit conditions during the 2019 and 2020 rice growing seasons to screen and identify the genotypes and determine the remarkable criteria for selection. The study revealed that environmental conditions, genotypes, and their interaction (GEI) mean squares were found to be highly significant for all physiological and morphological characters studied. Whereas, the genotypes that show high mean values for physiological and morphological characters are tolerant to water shortages. The grain yield plant-1 was investigated under normal and drought conditions, and it was positively significant associated with soluble sugar, peroxidase activity, the number of panicles plant-1, and 100-grain weight under drought conditions. On the other hand, a significant negative correlation was observed with soluble sugar, catalase activity, peroxidase activity, stomatal conductance, Na content, and sterility percentage under both normal and drought conditions, respectively.


Paper ID- AMA-17-03-2023-12131

The field experiment was conducted to confirm the optimum sowing date for chickpea to determine the infestation of H. armigera. It was found that the incidence and population fluctuations of this pest in both successive years were highly dependent on the prevailing weather parameters during the growing season of all three sowing dates. The minimum egg population was recorded as early sown crop on November 15th which was significantly superior over the other dates of sowing. Correlation analysis revealed that maximum and minimum temperatures exhibited significantly positive correlation with egg population of H. armigera in all three dates of sowing in both the year 2019-20 and 2020-21 except crop sown on 15th November and 30th November in 2019-20 in case of minimum temperature, while significant negative correlation was found with evening RH% on 30th November sown crop in 2019-20. The minimum larval population was recorded as early sown crop November 15 while highest larval population was observed with late sowing. The maximum temperature significantly positive correlation with all dates of sowing in both year of research 2019-20 and 2020-21. while evening RH% showed negative significant association chickpea sown on 30th November in the year of 2019-20. Whereas rainfall and rainy days exhibited negative correlation with mean larval population on December 15th in the year of 2019-20.

A Novel Aerial Object Detection Technique Using Deep Learning Method

Paper ID- AMA-16-03-2023-12129

Object detection in aerial images dataset is a challenging concept because of its dynamic behavior. This proposed work provides a novel way of aerial image detection in high spatial resolution aerial picture land-use/cover mapping using a method that is introduced to deal with the unique properties of aerial photographs, such as frequency domain content variability. Patch detection and description, in particular, are devised to partition and describe diverse sub-regions of objects made up of many homogenous components. In the present work we have proposed the VGG16 and its output is further feed to the Faster RCNN which makes the proposed model a novel work. Furthermore, the proposed bag of feature representation is built using statistics learned from the training dataset about the occurrence of the learning set of the image dataset. The analyses of several patch descriptors show that a mixture of spectral and textural characteristics is a good choice. In addition, to limit the impact of outliers on categorization in test data, a threshold-based technique is used. Experiments with data from aerial images are simulated and results are obtained using MATLAB 2021R software then the results are contrasted with the methods currently in use. The proposed method outperforms the existing work.