Research on Precise Intelligent Fertilization Recommendation Model Based on Big Data Association Analysis

DOI

10.2991/978-94-6463-980-3_2How to use a DOI?

Keywords

Precision fertilization; Agricultural big data; Association analysis; Tobacco cultivation

Abstract

This study proposes a precision intelligent fertilization recommendation model by integrating multi-source agricultural big data and association analysis techniques. Focusing on tobacco cultivation scenarios in Shandong Province (2010-2020), the research synthesizes soil nutrient dynamics (2013-2019 soil test results), regional fertilization protocols (e.g., Lanling, Pingyi, and Yinan fertilization packages), and production data to address the limitations of traditional empirical fertilization methods. A hybrid framework combining association rule mining, machine learning algorithms, and multi-objective optimization was developed to establish dynamic correlations between soil properties, crop requirements, and environmental factors. Key innovations include: 1) A data fusion mechanism for heterogeneous datasets (soil tests, fertilization records, meteorological data), achieving 92.3% prediction accuracy in nutrient demand; 2) An adaptive association analysis model that reduces fertilizer over-application by 18.7% while maintaining yield stability; 3) A cloud-edge collaborative decision system enabling real-time adjustments based on spatiotemporal variations. Validated across seven tobacco-growing regions, the model demonstrated a 23.5% improvement in fertilizer utilization efficiency and 15.2% yield increase compared to conventional methods. Theoretically, this work advances data-driven agricultural decision-making by establishing a cross-domain knowledge graph for fertilization. Practically, it provides a replicable paradigm for intelligent agricultural management systems, supporting sustainable intensification of cash crop production.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lei Tian
AU  - Xiaolei Tan
AU  - Lili Wang
AU  - Hao Zong
AU  - Xiwen Lu
PY  - 2025
DA  - 2025/12/26
TI  - Research on Precise Intelligent Fertilization Recommendation Model Based on Big Data Association Analysis
BT  - Proceedings of the 2025 5th International Conference on Business Administration and Data Science (BADS 2025)
PB  - Atlantis Press
SP  - 4
EP  - 11
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-980-3_2
DO  - 10.2991/978-94-6463-980-3_2
ID  - Tian2025
ER  -