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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
07 Dec 2022 (Vol - 53 , Issue- 12 )
Upcoming Publication
30 Nov 2022 (Vol - 53 , 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:

Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
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

Backstepping Sliding Mode Control - RBF Neural Network for Dual-Mass Systems

Paper ID- AMA-21-09-2021-10732

The success of the two-mass system control problem heavily depends on the accuracy information of the load torque. In the paper, a radial basis function neural network structure is proposed to deal with load torque estimation. The estimated value is integrated with backstepping- sliding mode control to guarantee speed tracking performance in the presence of a non-rigid driving shaft. The stability of the closed-loop is proven analytically and illustrated numerically. In addition, the effectiveness of the proposed control is compared with a high gain observer-based structure.

Weed Dynamics and Productivity of Potato as Influenced by Organic Sources of Nutrients and Weed Management

Paper ID- AMA-19-09-2021-10727

A two year field experiment was conducted during Rabi seasons of 2015-16 and 2016-17 to study the response of organic sources of nutrients and weed management on productivity and weed dynamics of potato. Application of 100% organics (100% recommended N through different organic sources each equivalent to 1/3 of recommended N i.e. FYM+ vermicompost + non edible oil cake) + VAM recorded significantly higher plant height, tuber yield, soil organic carbon, available N, P and K and nutrient uptake which was statistically at par with 100% organics (100% recommended N through different organic sources each equivalent to 1/3 of recommended N i.e. FYM+ vermicompost + non edible oil cake) + marigold for potato on border as trap crop and 100% organics (100% recommended N through different organic sources each equivalent to 1/3 of recommended N i.e. FYM+ vermicompost + non edible oil cake). Whereas, the application of 50% recommended N through vermicompost + biofertilizers for N + rock phosphate to substitute the P requirement + PSB recorded higher net returns and B: C ratio. Amongst the weed management treatments, application of mustard seed meal @ 5 t/ha resulted in highest plant height, tuber yield and soil organic carbon which was statistically at par with application of rice bran @ 4 t/ha and weed free treatment. Significantly lowest weed density, dry weight and nutrient uptake by weeds was recorded with weed free treatment followed by the application of mustard seed meal @ 5 t/ha and rice bran @ 4 t/ha. Among the weed management treatments, highest weed control efficiency was recorded with the application of mustard seed meal @ 5 t/ha followed by rice bran @ 4 t/ha.

Effect of irrigation scheduling on growth, yield and wateruse efficiency of Shatavar (Asparagus racemosus)

Paper ID- AMA-19-09-2021-10726

Asparagus racemosus belonging to family Liliaceae and commonly known as Shatavar, Shatavari, Satmuli, is a perennial climber found in the tropical and subtropical parts of India. Its tubers contain pharmaceutically active ingredients (steroidal saponins) and thus used in both traditional and modern system of medicines. In ayurvedic system of medicines, it is used as a galactagogue, aphrodisiac, anodyne, diuretic, antispasmodic and nervine tonic. The present investigation was conducted to assess the effect of irrigation scheduling on growth and yield of Shatavar. The experiment was conducted under Randomized Block Design with ten treatments and three replications. In the present study, 3 irrigation water depths i.e., 40mm, 50mm and 60mm were used to achieve IW and CPE ratio of 0.75, 1.0 and 1.25 in each depth. Maximum dry tuber yield ha-1 of 3.30 ton was recorded, when 60mm irrigation was given at IW/CPE of 1.00 (T9) but was statistically alike to T10 (3.10 ton) and T8 (3.01 ton). Minimum dry tuber yield ha-1 of 1.63 ton was observed in rainfed (control). Irrigation at 60mm depth given at IW, CPE=0.75 is beneficial to increase tuber yield based upon comparable yield and less water consumption. The crop can be grown as rainfed based on non-significant difference in WUE in different irrigation schedules.

Autonomous Detection of Cardia Ailments diagnosed by Electrocardiogram using various Supervised Machine Learning Algorithms

Paper ID- AMA-18-09-2021-10724

The electrocardiogram (ECG) signal is used to diagnose various Cardiac ailments as it holds the fundamental information to make appropriate decisions about different types of heart diseases. Hence several strategies were proposed to extract critical features from the ECG signal with highest accuracy which helps for the autonomous detection of Cardiac ailments. A methodology has been proposed in this work for state of the art in automatic detection of Cardiac ailments which include pre-processing, Feature extraction and Classification steps. A Butterworth third order band pass filter is used in pre-processing step and a four level Maximal overlap discrete wavelet packet transform (MODWPT) with symlet as mother wavelet is used for feature extraction step. Finally, for classification of considered three Cardiac ailments from MIT-BIH database i.e., Arrhythmia, Congestive Heart Failure and Atrial Fibrillation from Normal Sinus rhythm, five supervised Machine learning algorithms i.e., Support vector machine (SVM), K-nearest neighbour (KNN), Naive Bayes (NB), Decision tree (DT) and Random Forest (RF) were used which gives an overall accuracy of 90.83%, 90.56%, 90.28%, 91.39% and 91.94% for each classifier respectively. Clearly, random forest classifier for the proposed methodology gives better accuracy of the model for multiclass classification of cardiac ailments.

Optimization Study for Maximum Material Removal Speed when Grinding SKD11 tool steel with CBN Wheel on CNC Milling Machine

Paper ID- AMA-18-09-2021-10723

In this paper, the results of an optimization study when grinding SKD11 tool steel cylindrical parts with the use of CBN wheel and CNC milling machine. In the study, the maximum material removal speed (MRS) is the objective function. In addition, the influence of process parameters, including the spindle rotation speed, the depth of cut, the feed rate, and the wheel diameter on MRS was investigated. Moreover, optimal input parameters to achieve the maximum MRS have been proposed.