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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
16 Aug 2022 (Vol - 53 , Issue- 08 )
Upcoming Publication
31 Aug 2022 (Vol - 53 , Issue 08 )

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 Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
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

Rice Lodging Analyse Based on Gray Level Co-occurrence Matrix

Paper ID- AMA-16-12-2021-10964

Rice lodging refers to the phenomenon that rice crops growing upright are skewed and even the whole plant falls to the ground due to the influence of external forces. When the paddy field that has fallen down is harvested mechanically, it is necessary to adjust the harvesting direction, the height of the header and other factors according to the specific lodging situation. In the rice field image taken from the driving perspective of the harvester, the texture feature of the rice field image in the harvest area will rise or fall rapidly and shake greatly due to the lodging of part of the rice. In this paper, the gray level co-occurrence matrix analysis method and MATLAB simulation tools are used to analyze the texture characteristics of the four main factors of Angular Second Moment, Correlation, Inverse Different Moment and Contrast in the image of the area to be harvested from the driving perspective of the harvester. It provides a basis for determining the texture feature parameter criterion when using the neural network to detect the lodging situation of rice.

Design and Analysis of Energy-saving Forward Deep-rotary Tillage Machine

Paper ID- AMA-15-12-2021-10960

Rotary tillage operation generally has problems such as shallow tillage depth (8-15cm), low depth of mixed burying of straw, and high power consumption. Increasing the tilling depth by increasing the turning radius of the rotary tiller has problems such as huge mechanism and increased power consumption. In this paper, a deep rotary machine was designed, which could still achieve deep operation (tillage depth of 20cm) when the blade turning radius was reduced. Through the method of simulation and experiment, the article compared the operating performance of the deep-rotating machine and the common tool with increasing rotary radius. The result shown the advantages of the deep-rotating machine in the index of tillage depth and power consumption; the parameter pair of the deep-rotating machine was clarified. For the impact of job performance, the methods of variance analysis and response surface analysis were adopted to explore the relationship between parameters and indicators and fitted mathematical models. Field experiments were carried out and compared with the simulation analysis results. The errors between field test results and simulation analysis were 8.2%, 3.5% and 4.2% in power consumption, soil fragmentation rate and straw coverage rate. The average error in soil vertical displacement was 15.2%, and the longitudinal displacement error was 16.9%. The average error in the vertical distribution of straw is 15.7%.

Research on Mask-RCNN-Based Online Monitoring System for Wheat Impurity Rate and Breakage Rate

Paper ID- AMA-15-12-2021-10958

A sampling device that can collect high-quality images of wheat grain was designed, and the image was processed by Mask-RCNN algorithm. Then the calculation model which can calculate the impurity rate and breakage rate based on image information was established. Wheat grain images collected in the field were labeled and trained. The trained algorithm can identify the number of wheat kernels, the pixel area of damaged wheat kernels, and the pixel area of impurities in the image. Then the three data were input into the calculation model to obtain the impurity rate and damage rate. This protocol enabled the fast recognition and segmentation of wheat grain images with 90.46% recognition accuracy for intact wheat seeds, 88.85% and 90.32% segmentation accuracy for broken wheat seeds and impurities. The relative error of the method for monitoring the impurity rate and breakage rate of wheat is less than or equal to 9.86%. The research in this paper can monitor the wheat impurity rate and breakage rate when the combine harvester is harvesting wheat, which helps the driver adjust the combine harvester in time to improve the harvesting efficiency, and it can provide a reference basis for intelligent regulation of combine harvesters.

Study on the Influence of PCA Pre-treatment on Pig Face Identification with RF

Paper ID- AMA-14-12-2021-10954

To explore the application of traditional machine learning model in the intelligent management of pigs, in this paper, the influence of PCA pre-treatment on pig face identification with RF is studied. By testing method, the parameter of two testing schemes, one adopting RF alone and the other adopting RF+PCA, were determined to be 65 and 70, respectively. With individual identification tests carried out on 10 pigs respectively, accuracy, recall, and f1-score were increased by 2.66, 2.76, and 2.81 percentage points, respectively. Except for the slight increase in training time, the test time was reduced to 75% of the old scheme, and the efficiency of the optimized scheme was greatly improved. It indicates that PCA pre-treatment had a positive effect on improving the efficiency of individual pig identification with RF. It provides experimental support for the mobile terminals and embedded application of RF classifiers.

The Levers For Improving Irrigated Durum Wheat Performances: Evidence from Jendouba region in North Tunisia

Paper ID- AMA-13-12-2021-10953

In Tunisia, despite the outstanding development of the production technologies, the achieved yields of the irrigated durum wheat are still under expectations. This work aims to establish an operational diagnosis of the irrigated durum wheat activity and to identify alternatives for improving its performance. In order to deal with this issue, a field survey was carried out among a sample of 77 farmers cultivating durum wheat. Using CROPSYST software, a crop model was developed and scenarios of good practices were simulated. The results showed that we are able to rise the yields up to 20% and to improve the water productivity and the gross Margin. Hence results proved that matching water intakes and crop rotation constitutes relevant practices to ensure better agronomical and economical performances. The concretization of these paths requires a concerted reflection between the actors to put forward suitable strategies according to the studied context.