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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
18 Oct 2021 (Vol - 52 , Issue- 02 )
Upcoming Publication
31 Oct 2021 (Vol - 52 , 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
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.

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%.


Paper ID- AMA-12-08-2021-10609

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.

Effect of pre-harvest fruit bagging on maturity and physico-chemical properties of guava fruits cv. VNR Bihi

Paper ID- AMA-11-08-2021-10608

The study was conducted in a four-year-old guava trees cv. VNR Bihi planted at spacing of 5 m X 3 m at the Horticulture Research Centre, Pattharchatta, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Udam Singh Nagar, Uttarakhand during winter season, 2018-19. The experiment was laid out in randomized block design comprising of 17 treatments with 5 replications to evaluate the effect of different pre-harvest bagging materials on maturity and physico-chemical properties of guava (Psidium guajava L.). Various treatments were Biodegradable bags (purple, yellow, pink, green and red), Polypropylene bags (blue, light green, white, green and red) Polyethylene bags (yellow, white, blue, green, orange and pink) and without bagging (control). Among the various bagging materials, fruit length (9.05 cm), fruit diameter (9.61 cm), fruit weight (523.90 g), fruit volume (518.89 ml), specific gravity (1.01), flesh thickness (6.86 cm), seed cavity (5.84 cm), and the number of seed (325.23) were found significantly maximum in blue polyethylene bag treatment. Maximum TSS: acid ratio (31.55), and minimum titratable acidity (0.41 %) were significantly observed in the green polyethylene bag and yellow biodegradable bag, respectively. The maximum ascorbic acid (251.36 mg per 100 g) was also found significant in white polypropylene bag. Guava fruit wrapped with pink biodegradable bag gave maximum values of fruit firmness (3.61 kg/cm2), TSS (11.96 ºBrix), total sugar (11.15 %) and reducing sugar (5.01 %) content significantly with 10 days earlier fruit maturity compared to unbagged fruits. Based on the overall findings, it could be concluded that pre- harvest fruit bagging of guava cv. VNR Bihi with blue polyethylene or pink biodegradable bags were found best for the improvement of physical and chemical parameters of the fruits.