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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Reference Manager ProCite BibTeX RefWorks Toggle Thumbnails Select Design and implementation of farmland environment monitoring system based on NB-IoT LEI Juan HUBEI AGRICULTURAL SCIENCES    2022, 61 (14): 165-170.   DOI: 10.14088/j.cnki.issn0439-8114.2022.14.030 Abstract （416）      PDF （2380KB）（321）       Knowledge map    Save Farmland environmental information is an important basis for formulating farmland management strategies. In order to collect farmland environmental information in real time and stably, this paper designed and developed a remote monitoring system for farmland environmental information based on NB-IoT combining the advantages of NB-IoT. The system used STM32F103RCT6 MCU and sensor terminal to collect real-time farmland environmental data such as temperature, humidity, light intensity, carbon dioxide concentration, soil humidity, etc., and transmitited the collected data to the OneNET platform-based farmland environmental monitoring cloud platform through NB IoT network. Users can access the farmland environmental monitoring cloud platform through the farmland environmental monitoring App or PC to obtain the farmland environmental monitoring data. The system test results showed that the system could obtain real-time farmland environment information, such as temperature, humidity, light intensity, carbon dioxide concentration, soil humidity, etc. The temperature control accuracy was kept at a high level of ±0.2 ℃, and the relative error was 0.57%. The humidity control accuracy was kept at ±2% RH, and the relative error was 1.66%. The accuracy of light intensity control was kept at ±63 lx, and the relative error was 0.24%. The control accuracy of carbon dioxide concentration was kept at ±45.46 μmol/L, and the relative error was 0.34%. The accuracy of soil moisture control was kept at ±2%, and the relative error was 1.44%. The system has stable operation, real-time and accurate data transmission, practical function, and simple operation, and can be deployed on a large scale, which provides an effective reference for agricultural monitoring and internet of things application research. Reference | Related Articles | Metrics Select Named entity recognition in agriculture based on BERT embedding and adversarial training FEI Fan, YANG Lin-nan HUBEI AGRICULTURAL SCIENCES    2022, 61 (18): 196-202.   DOI: 10.14088/j.cnki.issn0439-8114.2022.18.035 Abstract （391）      PDF （3022KB）（439）       Knowledge map    Save Named entity recognition task for the agricultural domain is a key step in information extraction and question and answer system in the agricultural domain. The goal of this step is to find out the required named entities from a huge amount of unstructured agricultural texts, which usually have challenges such as diverse entity names and contextual semantics missing. To accomplish the task of recognizing agricultural named entities in complex contexts, this paper first constructs an annotated corpus in the agricultural domain, which contains 16 048 samples of six types of entities; then uses the BERT pre-trained language model as the word embedding layer, which can well solve the problem of different semantics and referents of the same word in different contexts compared with the traditional word embedding model; then uses the BiGRU network model for context encoding; finally, the output sequence is annotated using CRF. At the same time, this paper introduces a certain amount of noise to the input data, which is used for adversarial training to improve the generalization and robustness of the model. After the experiments, the accuracy, recall, and F-value of the proposed model are 92.75%, 91.53%, and 92.49%, respectively. Compared with the baseline model, this method has better performance and can effectively identify named entities in the agricultural field. Reference | Related Articles | Metrics page Page 1 of 35 Total 70 records First page Prev page Next page Last page