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. Shenyang Jianzhu Daxue Xuebao (Ziran Kexue Ban)/Journal of Shenyang Jianzhu University (Natural Science) General Medicine (ISSN:1311-1817) Chinese Journal of Evidence-Based Medicine Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption
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:
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.
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.
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.
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).
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.