WOS Indexed (2022)
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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
07 Dec 2022 (Vol - 53 , Issue- 12 )
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

Brain Tumour detection using Advanced Morphological Techniques

Paper ID- AMA-04-03-2022-11181

Brain tumour segmentation and detection requires efficient and rugged algorithms. All tumour images may not have same pixel intensity and contrast values. To identify the tumor in the brain is challenging task. Here, Brain Tumour detection using Advanced Morphological Techniques have been developed with histogram Normalisation, Thresholding, Fast Fourier transform (FFT), Fast Fourier transform(FFT) techniques. In this research, Length, Area of tumor and brain have been calculated and also calculated the statistical parameters. Also, proposed technique has been compared with the K-NN, NSC IN Gaussian Case, K-Means with Euclidian.

Optimization of the Metering Device of a Garlic (Allium sativum L.) Planter

Paper ID- AMA-04-03-2022-11180

This study determined the planting practices of Ilocos Norte, Philippines garlic farmers. Most farmers use the Ilocos White variety of garlic as planting material. Garlic fields range from 0.1 to 1 hectare with an average of 0.82 hectares. Planting distance range from 15.24 to 20.32 centimeters, with an average of 17.93 centimeters. This study also developed a garlic planter metering device in accordance with farmers’ inputs with a focus on determining the metering device’s optimal picker speed. The optimum speed should have a high percent singulation and low percent missed hills. Singulation is the ability of the cup conveyor to deliver only one clove to the delivery chute. Missed hills are deliveries without any cloves. Test speeds included 0.02, 0.035, and 0.05 m/s. A constant speed motor with different pulley systems drove the garlic planter on a test rig. Using the Design Expert Version 11 Response Surface – I-optimal Linear and Quadratic models, the optimal picker speed was determined to be 0.037 m/s with predicted values of percent singulation and missed hills as 71.27% and 5.88%, respectively. A confirmation test determined the actual performance of the metering device at the optimal picker speed. The percent singulation and percent missed hills of the planter in the confirmation test were 70.59% and 7.59%, respectively. ANOVA showed that the predicted values had no significant differences to the actual values.

Correlation coefficient and path analysis studies in okra (Abelmoschus esculentus L. monech)

Paper ID- AMA-02-03-2022-11177

The current study used twenty-five okra genotypes to evaluate the genetic variability, heritability, and genetic advance as a percentage of the mean. During rabi 2020-21, all twenty-five genotypes were evaluated in a randomized block design with three replications. For all the features, analysis of variance revealed a significant level of variability across the genotypes, indicating a broad range of variability across the genotypes. Number of fruits per plant has recorded the highest GCV and PCV followed by number of nodes per plant. This suggested that the environment had the least impact on the manifestation of these features. High heritability coupled with high genetic advance as per cent of mean were observed for number of fruits per plant, plant height, fruit length, number of ridges per fruit, 100 seed weight, number of nodes per plant, number of branches per plant, average fruit weight, fruit yield, fruit diameter, number of locules per fruit, days to 50% flowering, peduncle length, stem diameter at final fruit harvest, days to first fruit harvest. While in correlation studies number of nodes per plant followed by number of branches per plant, fruit diameter, number of ridges per fruits, average fruit weight, 100 seed weight has shown positive and high significant association with fruit yield per plant. Elsewhere fruit length has negative and highly significance. Path analysis revealed that positive direct effect on fruit yield per plant per plant was observed by number of nodes per plant, number of branches per plant, fruit diameter, number of ridges per fruit, number of fruits per plant, average fruit weight, 100 seed weight. Whereas fruit length followed by days to 50% flowering, peduncle length, days to first fruit harvesting, stem diameter at final harvest, fruit length has shown the negative direct effect on the fruit yield per plant. As a result, these traits should be prioritised in the selection of high-yielding okra genotypes.

Research on field path tracking control technology based on multi-sensor fusion

Paper ID- AMA-02-03-2022-11176

Aiming at the problem of low automation of field transportation, the path planning and tracking control technology of tracked transfer vehicle are studied. A control system based on ROS (robot operating system) platform is designed. The system integrates the information of global satellite navigation system (GNSS) and inertial navigation system (INS), realizes the real-time acquisition of the actual position of tracked transfer vehicle in Hilly and Mountainous Orchard field with high precision, and adopts the fuzzy proportional integral derivative (PID) controller based on parameter optimization preview to realize path tracking. The test results show that under the condition of normal positioning signal, the average lateral deviation of transfer vehicle path tracking is 10cm; When the positioning signal is abnormal, the transverse deviation tracked by the transfer vehicle is 10.56cm, which can achieve the goal of automatic driving of the transfer vehicle in orchards in Hilly and mountainous areas.

Real time Boron and Potassium Deficiency Detection in Plantain Trees Using Deep Convolutional Neural Networks

Paper ID- AMA-02-03-2022-11175

Predominantly, the identification of nutrient deficiency in plantain trees are based on visual symptoms. Nutrient anomalies are performed by trained specialists manually. It is a time-consuming process and needs proper attention since the symptoms appear in various parts of the crops. This work proposes an image classification and recognition system that analyses for analyzing nutrient deficiency from their visual appearance. The methodology uses deep learning techniques to analyze the nutritional status of the plant using the digital images of its leaves. Initially, the experiments are carried out for the boron and potassium nutrients deficiency of the plantain trees. The banana nutrient deficiency dataset consists of nutrient-deficient leaf images of plantain trees collected from various cultivation fields of Tamil Nadu. The efficacy of Convolutional Neural Network (CNN) makes them suitable for these kinds of applications. The system has experimented with state-of-the-art CNN architectures such as resnet50 and googlenet for classifying Boron and Potassium deficient plant images. The efficient architecture in terms of classification accuracy is selected for developing the SmartPhone-Centric real time nutrient deficiency detection system for assisting the farmers. The application presents the nutritional status of the plants, when getting the Plantain leaf images through the classification performed by the CNN. In the future, the dataset will expand by collecting the images of other nutrient deficient plants and will improve the classification accuracy with a greater number of images for obtaining more advantageous and expressive results for the real-time use of the farmers.