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
Impact of Weed Management Strategies Evaluated through Various Agronomic Indices in Direct Seeded Basmati Rice Preceded by Wheat in Sequence
A field experiment was conducted consecutively for two years in the Shivalik foot hill plains of Jammu and Kashmir on weed dynamics in direct seeded basmati rice that was preceded by wheat. In this study, the impact of different weed management strategies on crop productivity was assessed. The measures adopted to control weeds resulted in the notable enhancement in crop yields. In wheat, the combined application of Isoproturon @1.0 kg/ha + 2,4-D @ 0.500 L/ha as post emergence (30 DAS) was the most effective weed management approach that gave significant control of weeds which in turn increased crop yields. Weed density (no. of weeds/m2) and weed dry weight (g/m2) were recorded minimum with treatments Isoproturon @ 1.0 kg/ha + 2,4- D @ 0.500 L/ha which was statistically at par with mechanical weeding at 30 and 60 DAS. The same treatment produced significantly superior grain yield in comparison to all treatments under study. In the succeeding crop of direct seeded (DS) basmati rice, sequential application of Pendimethalin @ 1.0 kg/ha (PE) fb Bipyribac @ 0.030 kg/ha as post- emergence (at 30 DAS) turned out to be the most superior treatment that resulted in the highest suppression of weeds consequently maximum crop grain yields were recorded under the same treatment. Efficacy of different weed control interventions in both the crops were also reflected through various Agronomic Efficiency Indices used in the present investigation.
Effect of different organic farming packages on yield, biochemical properties and energy balance study under diversified cropping systems
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.
Sweet Sorghum: A Brief Study on a Smart Crop
The policy of the Government of India to reduce pollution and fuel-import cost by blending up to 20% ethanol and 80% gasoline as an automotive fuel which came into force 8 March 2021 [29]. In India there is continuous demand of alternative fuels to produce bioethanol from various feedstock’s. Currently, alcohol is produced only with molasses as the only feedstock. In addition, molasses also costs from Rs. 2000 to Rs. 5000 ton-1 excluding the cost of producing ethanol from molasses, which also varies. In addition, the ethanol production process is also not good for the environment because molasses also comes from the sugar industry as a byproduct. The sweet sorghum can accumulate up to 20% of sugars in its stalks [17] and can be a great alternative as a feedstock with minimal crop duration and water requirement as compared to sugarcane which is used to extract molasses. After extracting sweet sorghum juice, the bagasse contains higher calorific content. Hence, it can generate around 3.25 MW ha-1 of electricity [18]. In addition, several countries are looking for the steps to boost sweet sorghum’s production. Since stalk juice will only be used for producing ethanol, it will not affect food security. According to the Planning Commission of India, sweet sorghum is the best alternative to molasses for producing ethanol without affecting the environment in India [19, 20]. This article is aimed to explore the environmental and economical benefits of sweet sorghum and some challenges in ethanol production by conducting research on various studies and to find out the best steps that the Government of India can take to save the environment while fulfilling energy needs.
Cost Optimization Study for Worm – Helical Gearboxes
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
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).