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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. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology Research Journal of Chemistry and Environment

Submission Deadline
26 Jun 2024 (Vol - 55 , Issue- 06 )
Upcoming Publication
30 Jun 2024 (Vol - 55 , Issue 06 )

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:

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

Mapping of Rice Crop using Synthetic Aperture Radar (SAR) Data

Paper ID- AMA-28-04-2022-11331

Rice crop support the food need of billions of peoples and thus up-to-date and early statistics of acreage is essentially needed. Current study is being taken up to map the rice acreage for whole Haryana state covering a total of 4421200 ha area. Sentinel-1 SAR data were used for the mapping of Rice crop. A total of 28% of the state geographical area was found under the Rice coverage with a mapping accuracy of 90% using Maximum Likelihood (MXL) classification algorithm. Though the acreage was found to be less as compared to the statistical report of the year 2019 an early forecasting and yield assessment is possible using the current approach especially in monsoon season.

Research on lightweight sheep face recognition model based on double attention mechanism

Paper ID- AMA-27-04-2022-11330

To solve the problem that it is difficult to obtain the discriminant features of sheep face and the model is too large to be applied effectively in sheep face recognition. A lightweight sheep face recognition model based on double attention mechanism is proposed. The model is based on ShuffleNetV2 and add SKNet convolution attention module to adaptively adjust the convolution kernel size in order to capture sheep face features at different scales. Add an improved channel attention module in the channel dimension to suppress the interference of redundant information and enhance the expression of salient features of sheep face, so that the model could extract highly discriminant sheep face features. The model use Mish activation function to replace ReLU activation function to reduce the loss of feature information. Select adaptive scaling cosine metric function (Adacos) to train the model to speed up the convergence and further improve the recognition rate. On the constructed sheep face recognition datasets, the experimental results show that the recognition rate of the proposed sheep face recognition model is 91.52%, the model size is 4.45MB, and the computational quantity is 147MFLOPs. Experimental results show that the improved sheep face recognition model has better feature extraction ability and balance the recognition accuracy and complexity of the model, which can provide a reference for the practical application of sheep face recognition.

Moth Image Segmentation Based on Improved Unet

Paper ID- AMA-26-04-2022-11328

Moths are pests that pose a major threat to food production in China, and the monitoring and prevention of moth infestation is of great significance. To address the problems of a high diversity of moths with minor differences and difficult identification, a semantic segmentation network based on depthwise separable convolution, attention mechanism, pyramid pooling depthwise squeeze-and excitation pyramid network (DSEPNet)—was proposed. The network to extract texture features and wing edge information of moths was enhanced based on the optimization of the model of channel attention mechanism on UNet. The computational speed of the model was increased and the number of parameters of the model was reduced based on the improvement in depthwise separable convolution. A pyramid pooling module was added between the encoder and decoder so that the model could input images of an arbitrary size, while enhancing its ability to learn feature information of different dimensions. DSEPNet was evaluated by ablation and contrast experiments. Compared with UNet, the accuracy, mean intersection over union (mIoU), and F1-Score of DSEPNet were improved by 2.04%, 9.14%, and 4.08%, respectively. Based on the moth dataset, compared with R2AU-Net, the mIoU of DSEPNet was improved by 3.04%. To verify the generalization of the model, comparison experiments were done on the Pascal VOC 2012 dataset. The mIoU of DSEPNet was improved by 0.51% compared with PSPNet and by 0.18% compared with DeepLabv3. Meanwhile, an automatic annotation algorithm for data sets was proposed to solve the time-consuming and laborious process of manual annotation, which can automatically generate semantic segmentation annotation files. DSEPNet can be installed on the trap to identify moths in real time and improve the identification accuracy.

Fractional Calculus Cat Swarm Optimization based Neural Network for Removal of EEG Artefacts

Paper ID- AMA-24-04-2022-11326

In digital signal processing applications and biomedical research one of the challenging areas of research is Electroencephalogram (EEG) signal processing. Electroencephalogram (EEG) is a neurophysiologic measurement that records the EEG signal from electrodes placed on the scalp to study the electrical activity of the brain. The EEG signal is combined with other biological signals called artefacts. Removing artefacts from EEG signals is a crucial task in the medical field. To improve the quality of the EEG signal and to reduce the noise, a Fractional Calculus Cat Swarm (FCCS) based enhanced adaptive filtering neural network model is introduced. The EEG was initially fed into an adaptive filter NARX network, whose weights are renowned by the FCCS algorithm. The cat swarm optimization (CSO) algorithm plays a vital role in producing a better optimal solution and fractional calculus is included to incorporate previous weights in the updated solution of weights. Finally, the performance of the proposed method is compared with existing methods in terms of SNR, MSE, and correlation coefficient.

Evaluation of various synthetic Insecticides against White fly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) in Clusterbean

Paper ID- AMA-24-04-2022-11325

Experiments were conducted during three consecutive Kharif seasons at the Experimental Farm of Agricultural Research Station, Navgaon, Alwar (Rajasthan), to study the effect of commercially available insecticides formulations, Acetamaprid 20 % SP (1.0 gm/ litre of water), Imidacloprid 17.8 % SL (1.0 ml/ lit.), Quinalphos % 25 EC (2.0 ml/ lit.), Thiomethoxam 25 % WG (1.0 gm/ lit.), Neem oil 2% (20 ml/lit.), Karanj oil 2% (20 ml/lit.) against the White fly, Bemisia tabaci in Clusterbean. The descending order of most effective insecticides was: Imidacloprid > Thiomethoxam> Acetamaprid. During the 2015 year the maximum population reduction over control was found after 7 days of applying the second spray at 15 days of interval viz., 82.17 and 77.91 per cent due to Imidacloprid, Thiomethoxam, respectively. A similar trend was found in 2016 and 2017. Thus, Imidacloprid was found most effective against the White fly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae).