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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. 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 Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980)

Submission Deadline
10 Feb 2023 (Vol - 54 , Issue- 02 )
Upcoming Publication
03 Feb 2023 (Vol - 54 , Issue 01 )

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

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).

Callus mutagenesis using EMS and mutagenic response in aromatic rice (Oryza sativa L.) landraces of India

Paper ID- AMA-23-04-2022-11324

Induced mutagenesis in crop plants has created avenues for improving desirable genetic changes without altering the unique genetic background of the promising cultivars. A study was carried out to find the efficiency and effectiveness of 0.2% Ethyl Methane Sulfonate (EMS) mutagen on four popular but tall local aromatic rice landraces, Kalikati, Basumati, Gangabali and Karpurajeera by treating the calli initiated from their mature embryos for three different durations (2 hours, 4 hours and 6 hours). A reduced shoot regeneration efficiency was witnessed in Kalikati and Basumati (59.54% and 61.10% reduced respectively) while it increased in Gangabali and Karpurajeera (23.32% and 29.76 % increased respectively) with increasing treatment duration, compared to control. Among the four types of chlorophyll mutants observed, albina were most frequent in all the genotypes except Basumati, where virdis mutants followed by albina were highest whereas, in Kalikati, chlorina mutants were followed by albina in high frequency. In general, mutagenic effectiveness and efficiency were reduced with an increase in the duration of treatment in all the genotypes except in Basumati and Karpurajeera where mutagenic efficiency was highest at mid-treatment duration (4 hours). Mutation rate of 10.68 observed in Basumati was the highest among the genotypes indicated high mutagenic effect on the calli of this aromatic rice landrace. Genotypic differences in frequency of mutants, effectiveness and efficiency of the mutagen on the aromatic rice genotypes were clearly evident. This research will be useful in mutation breeding programmes, involving economically important crops, within a limited time and space.

Detection method of phenological distribution of Apple flower based on YOLO-CG

Paper ID- AMA-22-04-2022-11322

The estimation of crop phenological distribution is of great importance for controlling time of thinning flowers. In order to improve the efficiency of flower thinning in modern orchard, a detection method of apple flower phenological distribution based on YOLO-CG network model is proposed to detect, which aims at improving incomprehensive and low-efficient manual traditional detection method of apple flower phenological distribution. First of all, the YOLO-CG network model is to integrate the CA mechanism into the YOLOv5 network, which could obtain more shallow features to improve network performance; Secondly, in order to improve the training speed to reduce the calculation amount of the network model, the Ghost-Bottleneck module is proposed to replace the Bottleneck module; Finally, the CIOU is used as the bounding box regression loss function to improve the stability of the target box regression. The model is fine-tuned and trained with manually-marked apple flower images in 4 phenological stages. The proposed method was compared with the detection models of YOLOv3, YOLO v4, YOLO v5 and Faster R-CNN, and the detection performance of apple flower under different shooting conditions are discussed, which proves the effectiveness of this method. Experimental results show that the mAP value of apple flower detection at different stages was 94.90%, an increase of 1.98%, 7.1%, 5.42% and 2.53% respectively compared with Faster R-CNN, YOLO v3, YOLO v4 and YOLO v5.