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WOS Indexed (2023)
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. 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 Kongzhi yu Juece/Control and Decision Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980) Tobacco Science and Technology
26 Sep 2023 (Vol - 54 , Issue- 09 )
30 Sep 2023 (Vol - 54 , Issue 09 )
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 Asia Life Sciences Changjiang Liuyu Ziyuan Yu Huanjing/Resources and Environment in the Yangtze Valley Shenyang Jianzhu Daxue Xuebao (Ziran Kexue Ban)/Journal of Shenyang Jianzhu University (Natural Science) 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 General Medicine (ISSN:1311-1817)
Industrial Engineering Teikyo Medical Journal Tagliche Praxis Bulletin of National Institute of Health Sciences Journal of the Austrian Society of Agricultural Economics
Industrial and Commercial Design International Medical Journal Technology Reports of Kansai University Asia Life Sciences Open Access Journals
Information Engineering Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) Teikyo Medical Journal International Medical Journal Changjiang Liuyu Ziyuan Yu Huanjing/Resources and Environment in the Yangtze Valley Technology Reports of Kansai University
Chemical Engineering Open Access Journals Tagliche Praxis Bulletin of National Institute of Health Sciences Journal of the Austrian Society of Agricultural Economics Azerbaijan Medical Journal
Food Engineering Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980) Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) Kongzhi yu Juece/Control and Decision Tobacco Science and Technology Shenyang Jianzhu Daxue Xuebao (Ziran Kexue Ban)/Journal of Shenyang Jianzhu University (Natural Science) General Medicine (ISSN:1311-1817)
Soybean Gray Spot Disease Grading Based on Deep Voting Model
Soybean gray spot disease is one of the most common disease of soybean, which may affect soybean yield seriously. In order to reduce the economic loss caused by this disease and achieve a precision pesticide, it is necessary to grade the different level of gray spot disease. However, due to the similarities between different levels of disease, the automated grading of this disease is still a challenge. This paper proposes a deep voting model to grade different levels of soybean gray spot disease by weighted integrating some basic convolutional neural network (CNN) models with genetic algorithm. First, some basic CNN models are trained by transfer learning, and then three models with the highest accuracy are chosen to be integrated, with the optimal weighted parameters learned by genetic algorithm. The proposed model was trained and evaluated on a private dataset with 32000 images of four levels of soybean gray spot disease. The experimental results show that the grading accuracy of proposed model reaches 94.9% on the test set with 4800 images, which is about 4% higher than the accuracy of the basic CNN model. This study will facilitate the real-application of automated disease grading in precision agriculture.
Socio-Economic Assessment of Chickpea Growers in Banda District of Bundelkhand Region (Uttar Pradesh)
The present study carried out in the Banda district of Bundelkhand region of Uttar Pradesh. The chickpea is considered as the second most important pulse crop of the common beans. It is cultivated in over 50 countries throughout the tropical, subtropical and temperate regions. India is the largest producer consumer and importer of the pulses. The Banda district comprises eight blocks i.e. Badokharkhurd, Jaspura, Tindwari, Naraini, Mahuwa, Baberu, Bisanda and Kamasin. Among these, eight block Baberu block was selected purposively, because it had largest area and production in chickpea. From the Baberu block, six villages were selected randomly and from each village 20 chickpea growers were also selected randomly to Banda District. The variables of socioeconomic status of chickpea growers had revealed that the average age was found 54.64 years and marital status revealed the 96.67 per cent growers were married with average family size was found 8.25 members. It has also observed there 74.16 per cent of farm family were living as joint family and 92.50 per cent farm families belongs to hindu community. Further the study revealed 70.83 per cent chickpea growers were literate, 86 per cent growers were living in mixed and katcha house and the majority growers were engaged in the on farm activities, their average annual income was found Rs.134533.
Study on Working Condition Monitoring and Dosage Stability Control of Boom Sprayer
With the development of intelligent control technology, precision and intelligent requirements are put forward for traditional plant protection operations. Therefore, intelligent spraying technology has become an important research direction in the field of plant protection in the future. Based on the Internet of Things technology and measurement & control technology, a real-time monitoring system of the working state for the sprayer is designed in this paper, which can not only realize the remote monitoring of job status, but also upload the monitoring results to the remote server. The monitored parameters mainly include spray flow, spray pressure, machine speed, work trajectory, environmental wind speed et al. The overflow flow control device is built through flow valve and solenoid valve, and the target flow control is realized based on Fuzzy-PID control algorithm. The results of vehicle test show that the control error between the actual application amount and the target amount is less than 2% at different driving speeds.
Calculating Optimum Gear Ratios of A Two-stage Helical Gearbox for Minimum Gearbox Bottom Area
In this paper, the results of an optimization work to determine the optimal gear ratios of a two-stage helical gearbox to achieve the minimum gearbox bottom area are presented. To do that, a simulation experiment was designed and performed. The main design parameters including the face width coefficient of the firs and the second stages, the allowable contact stress of the first and the second stage, and the output torque were investigated. As a result, the influence of the above main design parameters on the optimal gear ratios has been evaluated. In addition, a regression formula to determine the optimal gear ratio for the first stage was found.
How Public Extension Services Affect Farm Income in Rural India?
Using 70th round of a nationally representative survey of farm households from the National Sample Survey Office (NSSO) conducted in 2013. This study examines the determining factors of participation in public extension services and their impact on farm income in rural India. To account for selection bias, the effect of participation in public extension services in terms of farm income in rural India was appraised using propensity score matching (PSM). The findings suggest that factors such as age, education, formal training in agriculture, higher consumption expenditure level, farm size, and exposure to mass media significantly determine participation in extension services. Further, it was also found that public extension services have significantly contributed to an increase in the farm income of the participants. These shreds of evidence provide an important impetus pointer from a policy perspective for the proper household targeting for participation in public extension services in rural India.