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.
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
The current research work sets the radar towards the finishing of jute fabric by application of aminosilicone softener by pad-dry-cure method, which was set after optimization of concentration, pH, material to liquor ratio, treatment temperature and time, drying temperature and time & curing temperature and time. The impregnation of the finish into the jute fabric resulted decrease in bending length, flexural rigidity and drape coefficient, thus causing improvement in softness and drapability of jute fabric. The finish also had a positive impact in controlling the colony forming units of Gram +ve bacteria (Staphylococcus aureus) and Gram -ve bacteria (Escherichia coli). There was no comparable difference in ultra-violet protection property of the treated jute fabric. The surface morphology was studied by scanning electron microscopy. This finish is of extreme importance because it not only improves the drawbacks of jute fabric but also it is environmentally benign and non-toxic.
A series of algorithms was implemented to plan optimal routes and generate smooth trajectories, for optimal and safe navigation of an agricultural robot. For the generation of optimal routes, the algorithm called A* was implemented, while for the smoothing of the trajectory the algorithm called Bezier curves was utilized. The system is capable of generating smooth trajectories with a resolution of up to one centimeter, in less than a second, performing by teleoperation the monitoring and remote control of the robot with frequencies of 2 Hz, which makes it suitable for robots in agricultural tasks. The developed software permits demarcating, from an aerial image, the crop rows or other obstacles present, and once the optimal route is generated to visualize its smoothing and geo-referencing, to provide a sequence of geographic coordinates, through which the robot must navigate.
The manual method of weed control is effective, but the scarcity of labour during the peak season and delay in weeding operations ultimately reduces crop yield. The use of chemical herbicides over a period of time leads to environmental pollution. Mechanical weeding is preferred over chemical use because herbicides are expensive and hazardous to the environment. Therefore, the development of mechanical weeder is imperative to meet the demand of small-farm mechanization. The available engine-operated mechanical weeder increase drudgery to the operator due to exposure to high levels of vibration. Hence, the complexity of these situations has resulted in switching over to an electric- drive mechanical weeding system to increase the productivity of the man-machine system. Therefore, a battery-operated inter-row weeder has been developed. It has an electro-mechanical approach such as the application of a DC motor as a power source and a combination of weeding mechanisms to complete the operation with less drudgery and higher efficiency. The field experiments were conducted in soybean crops under sandy-loam soil conditions. The field performance of weeder for V- type blade and straight blade tool were evaluated at the forward speed of 2-3 km/h. The results showed that the average weeding efficiency, field capacity, field efficiency, percentage plant damage, and performance index of the machine for V-type blade were found to be 91.42%, %, 0.051 ha/h, 90.73%, 2.37% and 2496.30, whereas for straight blade 86.78%, 0.048 ha/h, 88.50%, 3.55%, and 1497.04 respectively. The average power consumption of the weeder for V-type blade and the straight blade was found 185.6 and 262.5 W. The average draft force 269 and 391 N was observed for V-type and straight blades. The performance of the V-blade was found better over the straight blade. The developed weeder was found suitable for small-farm mechanization.
Bananas are commodities that are easily cultivated and developed in Indonesia. Optimizing plant growth is carried out through organic fertilizer and Arbuscular Mycorrhizal Fungi (AMF). This research aims to get the right type of organic fertilizer and AMF and understand whether there is an interaction between organic fertilizer and AMF dose to increase the growth of the banana plant Raja Bulu of tissue culture. The research was located in the experimental field of the Faculty of Agriculture, University of Sebelas Maret, Karanganyar. The research design used was a factorial, completely randomized design with a combination of 2 factors. The first factor is the type of organic fertilizers goat manure, cow manure, vermicompost, and compost. The second factor is the dose of AMF 0 g, 5 g, 10 g, and 15 g. The data obtained were tested statistically by statistical analysis with ANCOVA based on the F test level of 5%. If it had a real effect, further tests were carried out using DMRT with a 5% level. The variables observed were plant height, number of leaves, leaf width, leaf length, mycorrhizal infection, and stem diameter. Results showed an interaction between the types of organic fertilizer and the doses of AMF against the diameter of the stem of the plant of banana Raja Bulu. Goat manure can increase the growth of banana Raja Bulu better than the treatment of compost fertilizer. AMF dosage treatment of 5 g to 15 g per plant has not increased the growth of the banana Raja Bulu.
An image analysis algorithm for the classification of cherries in real time, by processing its digitalized color images, was developed, and tested. A set of five digitalized images of color pattern, corresponding to five color classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the fruit colored area. Histogram analysis was carried out for the RGB and HSV color spaces, wherein Red and Hue components showed differences between each of the specified color patterns, of the exporting reference system. This information allowed developing a hybrid Bayesian classification algorithm, based on the components R and H, and testing its accuracy with a set of cherry samples, within the color range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm it showed 100% of effectiveness in classifying a sample set of cherries, pertaining to the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, which permits an affordable commercial deployment.