Analysis of the Application of Big Data and Artificial Intelligence Based on Monitoring and Forecasting of Crop Pests and Diseases
- DOI
- 10.2991/978-94-6463-746-5_19How to use a DOI?
- Keywords
- Artificial intelligence; Crop pests and diseases; Monitoring and forecasting; Intelligent agricultural management
- Abstract
This study explores the application of big data and artificial intelligence in monitoring and forecasting of crop pests and diseases, aiming to improve the intelligence and management efficiency of agricultural production. A comprehensive monitoring and forecasting system was constructed by integrating farmland environment, meteorology and crop growth data. The system utilizes algorithms such as multiple regression, time series, cluster analysis and machine learning to accurately predict pest and disease trends and provide real-time warnings. The study shows that the system can improve the timeliness and accuracy of pest control, provide scientific support for agricultural decision-making, reduce pesticide use, protect the environment, and promote sustainable agricultural development.
- 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.
Cite this article
TY - CONF AU - Yuxiu Chen AU - Yiwen Wei AU - Miner Lin AU - Zhenhua Wen AU - Nuohan Zhang PY - 2025 DA - 2025/05/27 TI - Analysis of the Application of Big Data and Artificial Intelligence Based on Monitoring and Forecasting of Crop Pests and Diseases BT - Proceedings of the 2025 International Conference on Agriculture and Resource Economy (ICARE 2025) PB - Atlantis Press SP - 196 EP - 204 SN - 2468-5747 UR - https://doi.org/10.2991/978-94-6463-746-5_19 DO - 10.2991/978-94-6463-746-5_19 ID - Chen2025 ER -