Titlebook: New Developments of IT, IoT and ICT Applied to Agriculture; Proceedings of ICAIT Kazumi Nakamatsu,Roumen Kountchev,Bin Hu Conference procee

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Research on Pipe Surface Defect Recognition Based on Convolutional Neural NetworkThe experimental results show that the recognition rate of pipeline surface defects is higher after the application of convolutional neural network, which significantly improves the efficiency and accuracy of pipeline surface defect identification, and has good engineering significance.

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2190-3018 9”.Serves as a reference for researchers and practitioners iThe proceedings publishes new research results of scholars from the First International Conference on Agriculture and Information (ICAIT2019) organized by IRNet International Academic Communication Center, held during November 22-24, 2019.

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Paraconsistent Artificial Neural Network Applied to Agribusinessrch on international institutional sites, and research of data in national governmental institutions that work with the production, control, and export of this commodity. The biggest challenge of Agriculture 4.0 is to integrate technologies that can contribute to agribusiness. This is because farmer

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0.8 GHz Low-Noise Amplifier Design ratio ring on the basis of the conventional equal noise circle and the equal gain circle, it is possible to more intuitively select an appropriate impedance matching point in the same Smith chart. According to the above method, a narrowband low-noise amplifier operating at 0.8 GHz is designed.

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Optimization Analysis of Laying Length of Thin-Wall Micro-Spray Belt Under Different Slope Conditionope. When the laying length of the micro-spraying belt is arranged to L., the relative pressure P (L.) gets the maximum value. The paper put forward the laying length formula of the micro-spraying belt under different laying conditions—down slope, flat slope and adverse slope. The laying length of d

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The Research on the Judgment Method for Porcine Abnormal Diet Based on Improved PSO-SVDD porcine diet data are judged by the improved PSO-SVDD. The experimental results show that the proposed method obtains high accuracy for porcine diet data. It provides an effective method for the judgment of porcine abnormal diet through the diet data.

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