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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
04 Dec 2021 (Vol - 52 , Issue- 03 )
Upcoming Publication
31 Dec 2021 (Vol - 52 , Issue 03 )

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
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 different organic farming packages on yield, biochemical properties and energy balance study under diversified cropping systems

Paper ID- AMA-17-08-2021-10630

Considering the importance of organic farming and growing demand for organically produced quality foods, field studies were conducted for 2 years (2015-16 to 2016-17) on clay loam soil at the IFSRP, Rahuri, to study the effect of different organic farming packages on yield, biochemical properties and energy balance study under diversified cropping system. The highest total system productivity, biochemical properties and energy balance were obtained under onion - chickpea cropping system followed by onion – rabi sorghum with the application of 50 % N through FYM + 50 % N through vermicompost for kharif season crops followed by a direct effect of 100 % N through organic i.e. 50 % N through FYM + 50 % N through vermicompost to rabi season crops than rest of the treatments.

Cost Optimization Study for Worm – Helical Gearboxes

Paper ID- AMA-16-08-2021-10628

In this paper, a cost analysis of two-stage worm-gear box is presented. The optimal partial transmission ratios are determined to achieve minimum gearbox cost. Nine main parameters, including the total gearbox ratio, the coefficient of helical wheel face width, the allowable contact stress of helical gear set, the output torque, the gearbox housing cost per kilogram, the gear cost per kilogram, the coefficient of worm cost, and the shaft cost per kilogram, are considered in the optimization problem. The screen experiment technique is applied to carry out the design of experiment simulation. Finally, the regression model of the 2nd partial transmission ratio is obtained and utilized to find optimal values for minimizing the gearbox cost. Additionally, the results show that the output torque has the most significant effect on the ratio. The interactions among input parameters are also performed. Moreover, the proposed regression model is validated by the experimental data with excellent agreement.

Precision Farming through Early Agricultural Plant Leaf Disease Detection and Classification using Deep Learning Approach

Paper ID- AMA-15-08-2021-10626

Agriculture is a primary industry for sustainability and growth of humanity. High crop yielding is the basic requirement to feed the current population of this globe. Plants has a vital role to play in biodiversity sustenance. Precision farming or precision agriculture is the practice to maximize the crop yields and make agricultural profession more profitable. Precise and timely input of various agricultural parameters through smart and advanced technologies like IoT (Internet of Things), AI (Artificial Intelligence), Image Processing, Computer Vision, Drone based cameras, smart portable devices, GPS and others are providing precision farming a real playground for implementation. The practice of precision farming can boost the efficiency, sustainability, and profitability of farmlands. The vegetables and fruits plants not only demanded in agricultural productivity, but also in manufacturing of medical products, Cosmetics products, herbal and organic products and many more. Tomato is one major food crop in agricultural crops across the globe. There is approximately 20 kilogram per capita consumption per year of tomato and it represents 15% share in average total consumption of vegetables. To meet a huge demand of tomato worldwide, it is required to develop new techniques or improvise the existing ones for improving crop yield and early detection of diseases caused by viral infection, pests or bacteria. Early detection of such disease in tomato plant will help to increase productivity and quality of tomato. Convolutional Neural Network based models for recognizing and classifying of tomato leaves disease is proposed in this paper. Total 22930 images of tomato leaves (healthy and un-healthy) are used to train and validate the proposed CNN Based model and acquired an accuracy of 98.7%. ROC (Receiver Operating Characteristic) curve and other statistical parameters, including specificity, sensitivity, recall, precision and accuracy was applied to compare the performance of various pre-trained model used in transfer learning. The results are clearly indicates that AUC (Area Under Curve) values for implemented models are high (AUC>0.92).

Constraints Faced by Dairy Farmers in Haryana

Paper ID- AMA-15-08-2021-10625

Dairy production is the robust tool for the rural India to advance their financial as well as social status. The current study was conducted in the dry region and wet region of Haryana, were denoted as Zone-I and Zone-II respectively in this study. Zone-I includes the districts of Kaithal and Karnal. Zone-II comprises of Sirsa and Hisar districts to analyze the constraints faced by the dairy farmers in Haryana state. The present study was carried out by personally interviewing 200 dairy farmers, by adopting multi stage stratified random sampling technique taken on five-point Likert scale, IBM SPSS- 22 software was used for analysing the data by mean score percent. The findings of this study indicated that the major constraints faced by dairy farmers were production constraints, marketing constraints, financial constraints and other constraints like unavailability of skilled labor etc. With respect to production constraints, respondents perceived high cost of compound feed (57 %), lack of specialized dairy training (56.5%) and lack of knowledge about common contagious disease and their control measures (51.5%) as the most important constraint. With regard to financial constraints, the respondents reported that the high rate of interest (53%) was the most critical constraint followed by lengthy procedure for getting loan (63%) and inadequate land availability for fodder cultivation (30%). Poor marketing facilities (49%), lack of transportation facilities (32.5%), late payments received by producers (31.5%) and less remunerative prices of milk (20.5%) were the top 4 constraints under marketing constraints. With regard to other constraints, unavailability of skilled labor (53%) considered as the most important constraint by the farmers. The study revealed that among all the constraints, financial constraints is one of most important constraints and need to be focused more as majority of the respondents were facing problem regarding high rate of interest on dairy loans, dairy farming requires high initial investment hence dairy farmers needs credits. New policies should be framed for credit availability in dairy sector, followed by unavailability of skilled labor, production constraints and marketing constraints. The study suggested that trainings and awareness programs may be formulated frequently to the dairy farmers in the area with which they are more concerned.

Design of air-assisted side-deep fertilization control system based on parameter optimization

Paper ID- AMA-15-08-2021-10624

The traditional air-assisted side-deep fertilization device has some problems, such as inaccurate parameters of control system and poor precision of variable fertilization. It seriously affects the application and popularization of the device. Aiming at the above problems, this paper want to realize the precise control of air-assisted side-deep fertilization device. This paper constructs an electronically controlled fertilization system based on PID controller. The system model is built in MATLAB, and the transfer function of control system is deduced too. In order to improve the accuracy of control system parameters, the system parameters were optimized based on particle swarm optimization algorithm and control system tuner toolbox. The optimized system parameters of Kp, Ki, and Kd were set 3.05, 10 and 0.32, respectively. The experimental platform of variable fertilization was built according to the system parameters, and the mathematical model of variable fertilization was calibrated. The mathematical model’s R2 was 0.99. The optimized system parameters were imported into the controller, and the controller converted specified fertilizer quality to rotary speed of stepping motor. The driver ordered stepping motor to drive fertilizer discharge shaft at the set rotary speed by the controller. The experimental results showed that the maximum deviation of fertilization rate was 10.76g, which could better meet the requirements of precision fertilization. This research can improve the evenness and accuracy of the application of electric control fertilization.