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 (2025)
27 Nov 2025 (Vol - 56 , Issue- 11 )
30 Nov 2025 (Vol - 56 , Issue 11 )
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
Effect of pre-harvest fruit bagging on maturity and physico-chemical properties of guava fruits cv. VNR Bihi
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.
A DEEP LEARNING-BASED ALGORITHM FOR MONITORING CHEATING IN EXAMS
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.
Modelling and forecasting of rice production in south Asian countries
This study aimed mainly at forecasting rice production in SAARC and Iran and is linked to the univariate time series prevision. The rice production forecast was done with the BATS and TBATS models, Holt's Linear Trend, (NNAR) model and ARIMA model. All the data series have been divided into training sets for model construction purposes between 1961 and 201 4, and testing sets for validation purposes between 2015 and 2019, and after the best model was selected, prediction was used between 2020 and 2025. From the forecast values, we conclude that rice production in SAARC countries including Iran will continue to increase in the next couple of years. In Pakistan, the highest production growth rate was estimated at 2.59%, while in Afghanistan the lowest growth rate was estimated at 0.13%. The highest SAARC country rice producer, India, is estimated to increase by 2.24 per cent. These findings may be important in building a successful policy on production by providing an idea of the expected production values or by assessing the performance of these policies and by predicting the food gap in rice crops.
State of art of ARIMA modeling in top five egg production countries in World
Eggs constitute a good source for animal proteins and are considered as a food of great nutritional value. In this way, Eggs production has shown a marked increasing in the last years through the world. The objective of this work is to predict eggs production in the next years in the five highest producing countries in the world including China, the Unites States of America, India, Mexico and Indonesia using time series data from 1961 to 2019. Five ARIMA models (1,2,0), (5,2,0), (1,2,1), (4,2,1) and (2,2,0) were used. Results showed that the best model for China, India, Indonesia, Mexico and United States of America are ARIMA (1,2,0), (5,2,0), (1,2,1), (4,2,1) and (2,2,0) respectively. Eggs production prevision for the cited countries showed that except for the United States of America where the production would decrease for about (- 2.6%), an increasing of production of (16.7%), (15%), (13,7%) and (11.5%) would be observed in 2026 in China, India, Indonesia and Mexico respectively. The estimated production willbe 930104, 100920,83806, 71402, 42855 million numbers in 2026in China, India, United States, Mexico and Indonesia respectively. This study showed the importance of ARIMA models in the prediction of eggs production which could be helpful for decision-makers in planning for future food policy.
Intel Mot: An Internet of Things Based Smart Watering System Using Decision Trees
Watering to the plants in the agriculture field is done promptly in conventional farming, without considering important parameters such as the water requirement of a crop, the possibility of rain on the next day, etc. In some instances, soil moisture, humidity, and temperature are taken into consideration for crop watering which may not be sufficient to conserve the water. The atmospheric conditions play a main role in the water requirements of the crop. The internet of things and machine learning are extensively becoming popular during the last decade and find applications in all domains such as agriculture, banking, smart home, etc. Internet of things is used in this agriculture application, where the data from soil moisture sensor, humidity, and temperature sensor is combined along with weather data to water the crops. In this research paper, an IoT-based smart watering system for assessing the watering requirements of the crop is proposed. The soil moisture parameters in multi-crop environments at various locations are measured using a ground moving robot with a moisture sensor embedded in it. Information gain and entropy statistics of the decision tree are applied to find the status of the output actuator (sprinkler motor). Entropy methods make our system more efficient by using decision tree split criteria, and it is implemented using MATLAB where the information gain and entropy are used for the selection of the best feature to split the tree. Accuracy rate and decision tree performance are improved by the efficient feature split of decision trees. An accuracy of 96.5 % is achieved for the proposed system. The proposed model is a low-cost system where a single ground moving robot is used to collect the crop parameters. The system will reduce the time a farmer spent in the agriculture field as he need not visit the farm regularly.