WOS Indexed (2022)
clarivate analytics

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
24 Nov 2022 (Vol - 53 , Issue- 11 )
Upcoming Publication
30 Nov 2022 (Vol - 53 , 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:

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

Visual perception, comprehension and gain in knowledge of Video Programme on Management of Organic Farming for Rural Women

Paper ID- AMA-19-11-2021-10853

The study was conducted in randomly selected two villages of (Palana and Barsinghsar) of Bikaner Panchayat samiti with a sample of 60 rural women (30 from each village). Pre and Post- test experimental research design was used for the present study. To find out the visual perception and comprehension unstructured open ended questions were framed. Pre-test was done by the help of developed knowledge test with the rural women, to know the existing knowledge on organic farming and after this developed video programme was exposed to the rural women. Post-test was carried out to find out the gain in knowledge. Regarding visual perception and comprehension findings indicate that majority of respondents have perceived and comprehended the messages of video programme very well. Significant improvement in the knowledge of respondents was found as a result of exposure of video programme with the increased in mean percent score is from 22 to 62.90 per cent with the gain in knowledge of about 40.90 per cent. On the basis of these findings, it is concluded that the developed video programme on "Management of organic farming" was good and is useful for field functionaries, extension workers, and all those agencies / organization working in rural areas for transfer of scientific information to rural women.

A New Variable Selection Method for Near Infrared Spectral Analysis Based on Three-step Hybrid Strategy

Paper ID- AMA-19-11-2021-10848

A specific variable selection method was proposed based on three-step hybrid strategy for near infrared spectral analysis. By analyzing functions of each step and characteristics of various variable selection methods, synergy interval partial least squares, iterative variable subset optimization and bootstrapping soft shrinkage were chosen for three steps. To test effect of the three-step hybrid method, it was applied into corn and soil spectral data and compared to other common methods. Results showed that three-step hybrid variable selection method selected 1% variables of full spectrum, calibration determination coefficient and prediction determination coefficient reached 0.9976 and 0.9932. It could effectively extract variables related to tested substance and provide a new variable selection method for near infrared spectral analysis.

Neural network analysis of mechanization level-a Case Study in Northwest regions of Tunisia

Paper ID- AMA-17-11-2021-10846

Agricultural mechanization level in two governorates in northwestern Tunisia has been assessed and analyzed. Interviews, observations, and a structured questionnaire were used for the collection of the database. Five artificial neural networks (ANN) models with two hidden layers were used to estimate the mechanization level. Initially, 80 attributes were used as input variables to predict the level of mechanization. The Forward regression method (SPSS 22 software) followed by the significance analysis method was used to select the typical variables. Ten variables were selected as models’ inputs. The performance of the ANN model was evaluated with various statistical measures including coefficient of determination (R2), mean square error (MSE), and mean absolute error (MAE). The optimal ANN models had correlations of 0.94 with calculated mechanization levels. Sensitivity analysis of the models showed that farm area, labor, dominant crop area, and the number of tractors is the typical inputs affecting the level of mechanization. The results presented in this study justify the low level of mechanization according to farm area (Maximum value less than 30%), dominant crop area (average value less than 25%), number of tractors (Maximum value less than 30%). However, the availability of labor gives an acceptable level (on average 50%) for farms with some skilled labor between 3 and 4 and a low mechanization level for farms with some skilled labor is less than 2 (on average 10%).

Modified UNet Design for Semantic Segmentation for Brain Tumour Detection

Paper ID- AMA-17-11-2021-10845

The identification of the tumour extent is a fundamental difficulty in brain tumour treatment planning and accurate measurement. Without ionising radiation, non-invasive magnetic resonance imaging (MRI) has emerged as a first-line diagnostic method for brain malignancies. Manually segmenting the extent of a brain tumour from 3D MRI volumes is a time-consuming job that heavily relies on the operator's knowledge. So, in this paper, we proposed a modified UNet structure that is based on residual networks that use shuffling periodically at encoder section of original UNet and sub-pixel convolution at decoder section. Sub-pixel convolution has the benefit over conventional resize convolution and it has extra parameters and thus stronger modelling capability at the same computing complexity and avoids de-convolution overlapping. The proposed UNet was tested on BraTS Challenge 2017 with high- grade glioma (HGG). The model was tested on BraTS 2017 and 2018 datasets. Tumour core (TC), whole tumour (WT), and enhancing core (EC) were the three major labels to be segmented (EC). The test results showed that the proposed UNet outperform the existing techniques.

RAPID RECOGNITION OF SPROUTING POTATO IMAGES BASED ON IMPROVED YOLO V5

Paper ID- AMA-16-11-2021-10844

Sprouted potato detection is an essential measure before potatoes enter warehouse storage and can effectively reduce the chance of warehouse spoilage of potatoes. How to detect potato health intelligently and efficiently is important to improve the quality of potatoes before they enter the warehouse. To achieve the detection and grading of sprouted potatoes in a variety of complex scenarios, this study proposes a sprouted potato detection algorithm based on an improved YOLOV5 model. The Cross Conv module with improved feature similarity is used to replace the Conv of the original C3 module of YOLOV5, which improves the similarity loss problem in the fusion process and increases the feature expression capability; the SPPF with accelerated space pyramid is used instead of SPP for fusion pooling, which reduces the number of fusion parameters and accelerates the speed of fusion pooling; the 9-Mosaic algorithm is enhanced and optimized to strengthen small target features before the image enters Backbone; then the accuracy is further improved using hyperparametric evolution with genetic evolution anchor points and multi-scale training mechanism, and the experimental results show that: the improved model recognition accuracy reaches a minimum of 90.14%, the average accuracy of the whole class mAP@.5 reaches 88%, and the F1-score is 84%, which is higher than the original YOLOV5 network in the same test dataset The model mAP@.5 index is 7.4% higher than the original model under the same test dataset, which has obvious advantages over the existing model. The real-time sprouting potato image recognition based on improved YOLOV5 proposed in this study has good accuracy and effectiveness, which can basically meet the requirements for establishing automatic potato sorting line and realizing high-throughput and fast potato classification, and provide technical reference for intelligent agricultural equipment in modern agricultural environment