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HTTP状态 404 - 未找到


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Apache Tomcat

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    Missing data filling algorithm for soil temperature and humidity based on CNN-LSTM Model
    ZHANG Ying-jin, SHI Zhi-qiang, Gulimila Kezierbieke, Kumusi Ayiken
    HUBEI AGRICULTURAL SCIENCES    2025, 64 (2): 179-183.   DOI: 10.14088/j.cnki.issn0439-8114.2025.02.028
    Abstract86)      PDF (2405KB)(20)       Save
    A convolutional neural network-based long short-term memory network (CNN-LSTM) filling model was proposed to address the problem of soil temperature and humidity sensor data loss caused by harsh environments, battery depletion, hardware failures, and other factors. Using the soil temperature and humidity data from the Shandian River Basin in 2019 as experimental data, five models including CNN, LSTM, TCN, CNN-TCN, and CNN-LSTM were selected to fill in the missing data of the soil temperature and humidity sensor network. The Adam algorithm was used to optimize the model, and the coefficient of determination (R2), mean square root error (RMSE), and mean absolute error (MAE) index were used to evaluate the results of the model filling. The results showed that using the linear interpolation algorithm to obtain complete data, the R2 of the CNN-LSTM model was 0.999 9, which was higher than that of other models. The MAE and RMSE were 0.001 85 and 0.019 70, respectively, which were much lower than those of other models. The K-nearest neighbor interpolation algorithm was used to obtain complete data. The MAE and RMSE of the CNN-LSTM model were 0.000 12 and 0.000 12, respectively, which were much lower than those of other models. The R2 was 0.999 4, which was higher than that of the CNN model, and TCN model;the CNN-LSTM model had the best filling effect on missing values in soil temperature and humidity sensor data. The CNN-LSTM model had good feasibility and accuracy in dealing with the problem of missing data from large-scale soil temperature and humidity sensors.
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    Design and implementation of farmland informatization and crop traceability system based on WebGIS
    ZHOU Jun, PU Zhi, LEI Zhuo-yue
    HUBEI AGRICULTURAL SCIENCES    2025, 64 (2): 184-191.   DOI: 10.14088/j.cnki.issn0439-8114.2025.02.029
    Abstract73)      PDF (11169KB)(12)       Save
    In response to the problems of lagging map updates, low utilization of geographic information resources, scattered and disorderly agricultural data, and lack of effective crop traceability methods in farm management, a WebGIS-based farmland informatization and crop traceability system was constructed based on Django framework, OpenLayers map library, PostgreSQL database, PostGIS extension tool, and GeoServer platform. The system was based on data collection and farm map construction, and its main functions included map operation, land parcel information query, dynamic display of land parcel information, path planning, crop traceability, and one map of farm information resources. The system had the characteristics of convenient operation, easy maintenance, and easy scalability, and fully considered the unique attributes and regional differences of various farms in its design, with wide applicability. This system could achieve precise control of farm maps, efficient management and visualization of farm data, and obtain complete crop traceability records. Combined with WebGIS technology, the system provided a scientific and efficient operation management system for farm managers, effectively promoting the informationization and modernization of farms.
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