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.
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
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%.
This paper presents a CPW-fed antenna with enhanced gain for sub-6GHz band wireless and vehicular safety applications. In the antenna design, partial removal of substrate technique is incorporated to obtain improved gain values and omnidirectional characteristics. Initially, a basic CPW-fed antenna is designed, which operates in the band of frequencies from 3 GHz to 6.41 GHz exhibits poor impedance matching. This antenna radiates lesser gain values at the upper resonating band above 5.6 GHz. Further improvement of broadside gain can be enhanced by a factor of 2.49 dB at 5.9 GHz DSRC band using partial removal of substrate technique. And the antenna impedance matching is greatly improved in the range of frequencies from 2.71 GHz to 7.43 GHz. The measurement results of the fabricated prototype show a good match with simulated results. The antenna is located on the vehicle (car) rooftop in a virtual atmosphere with the help of the ANSYS SAVANT tool and investigated 2D-radiation patterns and 3D-gain plots. The same antenna is located at two positions of the car model and obtained the antenna to antenna coupling factor, which is well below -30 dB.
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.
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.
With the rapid growth of the Internet and technologies over the last couple of decades, the trend of cheating in the examination is changing and increasing exponentially. The new technologies provide new means of cheating, and students can learn about different ways to avoid getting caught from social media, causing an upsurge in the cheating trend. Data studied in this work has been collected from an academic institution where examinations are supposed to be held under the invigilation of an invigilator. A method for predicting cheating activities has been proposed in this study. This method monitors the physical activities performed by the students during an examination, detects any anomalies relevant to cheating, and classifies them into different categories. This method integrates resizing the images in the whole dataset and their conversion to grayscale images, feature extraction, selection, and classification of images. Features are extracted by applying feature descriptors such as Local binary patterns and texture features (SFTA and Gabor). Afterward, the fused features are selected by implementing the Principal Component Analysis method. Lastly, the selected features are classified using a support vector machine and Fine KNN. The proposed method uses the newly created dataset to evaluate its effectiveness. This approach has provided a promising score of accuracy 73.8%, sensitivity 69.7%, specificity 6.06%, and F-Score 71.76%. This method detects and classifies the cheating activities performed by the students during their exams. This study however can also be used to monitor physical activities performed by students on various occasions such as the representation of different cultures in sports week.