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Research on Intelligent Analysis Models for Crop and Yield Based on Multi-Source Data Fusion and Time-Series Forecasting

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DOI: 10.23977/acss.2026.100208 | Downloads: 0 | Views: 51

Author(s)

Ji Baitong 1, Tao Ye 1, Liang Can 1, Wu Ruihao 1, Li Dongjun 1

Affiliation(s)

1 School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, China

Corresponding Author

Ji Baitong

ABSTRACT

To achieve precise prediction of growth trends and scientific assessment of yield in agricultural production, this paper proposes a crop growth and yield analysis model based on multi-source data fusion and time series forecasting. The model integrates multimodal information such as meteorological data, soil moisture, historical yield, and equipment monitoring, constructing a complete analytical pipeline of "data acquisition—feature fusion—trend forecasting—decision output". By introducing a hybrid model of LSTM and XGBoost, dynamic prediction of regional rice yield and identification of growth stages are realized. Experimental results demonstrate that the model exhibits high prediction accuracy and strong generalization capability on actual data from multiple agricultural regions, providing effective technical support for precision agricultural management.

KEYWORDS

Multi-source data fusion; Time series forecasting; LSTM; XGBoost; Yield prediction; Agricultural monitoring system

CITE THIS PAPER

Ji Baitong, Tao Ye, Liang Can, Wu Ruihao, Li Dongjun. Research on Intelligent Analysis Models for Crop and Yield Based on Multi-Source Data Fusion and Time-Series Forecasting. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 2, 73-78. DOI: http://dx.doi.org/10.23977/acss.2026.100208.

REFERENCES

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[3] Zhang Jianguo, Li Jian. Research on agricultural meteorological disaster early warning system based on multi-source data fusion[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(12): 1-9. (Chinese)
[4] Wang Lei, Zhao Lihan. Research on crop yield prediction model based on LSTM[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(6): 234-241. (Chinese)
[5] Chen Mingjun, Liu Yang. Research progress on crop growth monitoring and yield estimation by fusing multi-source remote sensing data[J]. Scientia Agricultura Sinica, 2023, 56(2): 289-302. (Chinese)
[6] Zhou Tao, Li Jun. A review of agricultural time series prediction methods based on deep learning[J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(8): 156-167. (Chinese)

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