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 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)
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:
Digging is one of an important part of afforestation. To promote mechanized afforestation in hilly regions, this paper study the work process and the principle of the auger during the drilling under the ground based on the discrete element method (DEM). The effect of spiral blade geometry on soil disturbance and soil-soil soil-tool interaction forces in a cohesive soil was assessed in a virtual soil bin using the DEM. Combined with the mechanical analysis and simulation results, the soil throw trends, the energy change, the influence of parameters is analyzed on the spiral blades. The main influencing factors for soil uplift are the drilling speed, the helix angle of spiral blade, the soil characteristics, and the feed velocity. Based on that, this study also explores the formation conditions and mechanism of the fish-scale pits on the slope to the soil and water conservation. To test the result, a verification experiment has been carried out in arid and semi-arid hilly forest areas. This research provides a theoretical reference for the innovative research, development, and design optimization of the earth auger in hilly regions.
This paper presents the results of an optimization problem to determine the optimal gear ratios of a four-stage helical gearbox to achieve the minimum gearbox bottom area. To do that, a simulation experiment was conducted. In addition, the influence of six main design parameters including the surface width coefficient of the first and second stages, the allowable contact stresses of the first and second stages, and the output torque have been evaluated. Moreover, regression equations to determine the optimal gear ratios of the gearbox have been proposed.
One of the top pest concerns in food production and storage across the globe, is stored product insects because they cause substantial damage and contamination. Insect infestations induce changes to the storage environment leading to warm, moist conditions which are suitable for fungal growth that further causes hazardous effects. The use of chemical insecticides to control pests can cause toxicity hazards to non-target organisms and serious health problems for humans. From this perspective, we assessed the effect of three botanical powders, Boswellia carterii (Sapindales Burseraceae), Elettaria cardamomum (Zingiberales: Zingiberaceae) and Pistacia lentiscus (Sapindales Anacardiaceae) against two stored-product pests, granary weevil Sitophilus granarius (Coleoptera Curculionidae) and confused flour beetle Tribolium confusum (Coleoptera Tenebrionidae). Both insects were affected by different powder concentrations, time of exposure, and the bioactivity of the plant components. Adult S. granarius were more sensitive to different powder concentrations than those of T. confusum. Concentrations of 15% B. carterii, E. cardamomum and P. lentiscus powders caused 73.3, 80, and 100% mortality of T. confusum after 14 days post treatment, respectively. While the same concentrations of the three botanical powders caused 100, 96.7, and 100% mortality of S. granarius. P. lentiscus was more effective than B. carterii and E. cardamomum. This study showed that B. carterii, E. cardamomum and P. lentiscus powders had biological and toxicological effects against S. granarius and T. confusum. These plant powders are considered safe for human use.
This paper reports an optimization study to find the optimal gear ratio of a three-stage helical gearbox to achieve the minimum gearbox bottom area. In this study, a simulation experiment was built and performed. Also, six main design parameters including the surface width coefficient of the first and second stages, the allowable contact stresses of the first and second stages, and the output torque were selected for the investigation. The influence of these parameters on the optimal gear ratios of the gearbox has been shown. Moreover, the optimal gear ratio u1 of the first stage can be easily calculated by means of a regression formula in the form of an explicit function.
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.