Two-Level Distributed Multi-Source Information Fusion Model for Aphid Monitoring and Forecasting in the Greenhouse

文献类型： 外文期刊

第一作者： Li, Xiaoyin

作者： Li, Xiaoyin;Wang, Lixing;Dai, Min;Miao, Hong;Dai, Min;Miao, Hong;Zhang, Yongji;Su, Wei;Wang, Mingyou

作者机构：

关键词： aphid monitoring and forecasting; multi-source information fusion; one-dimensional convolutional neural network (1D CNN); Long Short-Term Memory (LSTM); agriculture; greenhouse

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 5 期

页码：

收录情况： SCI

摘要： Aphids are the main agricultural pests that affect the quality and yield of peppers in the greenhouse. Efficient early prediction of aphid occurrence is of great significance for the development of digitization and information technology in intelligent agriculture. Forecasting accuracy could be improved by the incorporation of feature interactions into pest forecasting. This study integrates multiple environmental factors to efficiently predict the number of aphids and the aphid strain rate in the greenhouse. We propose a two-level distributed multi-source information fusion approach, which integrates a one-dimensional convolutional neural network (1D CNN) and Long Short-Term Memory (LSTM). To enhance the accuracy of regional environmental parameters, a weighted average algorithm employs environmental sensor data in the first level of fusion. In the second fusion level, a heterogeneous sensor fusion algorithm allows for the integration of multi-source data to model the connection between environmental factors and aphid dynamics. Finally, the improved 1D CNN-LSTM fusion model and other models were tested to verify the effectiveness and robustness of the proposed model. The experimental results show that the total root mean square error of the proposed model is 1.503, which is obviously better than the other networks. In the test set, the total root mean square error of the model for predicting the aphid number and strain rate is 1.378 and 0.337, respectively, compared with existing network models such as 1D CNN, LSTM, and back propagation (BP). The experimental results show that the proposed model has obvious advantages for predicting the aphid number and strain rate. It provides a promising step forward in pest management, offering precise, environmentally friendly solutions that enhance crop yield and quality.

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