Multimodal Data Fusion for Intelligent Early Warning System of Cucumber Diseases
DOI:
https://doi.org/10.63313/JCSFT.9088Keywords:
Multimodal Data Fusion, Convolutional Neural Network, Cucumber Disease Recognition, Intelligent Early Warning, Attention MechanismAbstract
To address the limitations of traditional cucumber disease recognition relying solely on image information with insufficient early warning accuracy, this paper proposes an intelligent early warning system for cucumber diseases based on multimodal data fusion. The system integrates leaf image data with environmental sensor data (temperature, humidity, light intensity, CO₂ concentration) and constructs an Attention Mechanism-based Convolutional Neural Network (AM-CNN). A multimodal dataset containing 12,000 samples across 7 disease categories was built by collecting cucumber leaf images and corresponding environmental parameters. Experimental results demonstrate that the AM-CNN model with multimodal fusion achieves an average recognition accuracy of 94.7%, which is 2.4 percentage points higher than the single-modal CNN model and 15.6-20.8 percentage points higher than traditional machine learning algorithms. The end-to-end response time is 1.6 seconds, meeting real-time monitoring requirements. This research provides a novel technical solution for precision disease warning in facility agriculture, with significant theoretical and practical value.
References
[1] Ministry of Agriculture and Rural Affairs, China. (2023). 2022 National Vegetable Industry Development Report. Beijing.
[2] Li B., & Shi Y. (2021). Current status and prevention strategies of vegetable diseases in China. China Vegetables, (1), 1-7.
[3] Mohanty S. P., Hughes D. P., & Salathé M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
[4] Ferentinos K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318.
[5] Zhang Z., & Liu W. (2019). Design of intelligent monitoring system for greenhouse based on IoT. Transducers and Microsystem Technologies, 38(6), 112-115.
[6] Li M., & Chen X. (2020). Tomato leaf disease recognition system based on CNN. Transactions of the Chinese Society of Agricultural Engineering, 36(12), 188-195.
[7] Wang M., & Zhang J. (2021). Multi-modal data fusion for crop yield prediction. Computers and Electronics in Agriculture, 189, 106324.
[8] Chen L., & Liu Y. (2022). Precision irrigation based on multi-sensor data fusion. Agricultural Water Management, 262, 107536.
[9] Vaswani A., Shazeer N., Parmar N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
[10] Li B., & Chen X. (2022). Attention-based CNN for plant disease recognition. Computers and Electronics in Agriculture, 198, 107245.
[11] He K., Zhang X., Ren S., & Sun J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.
[12] Zhao P., & Wang F. (2020). Design of environmental parameter monitoring system for agricultural greenhouse based on STM32. Application of Electronic Technique, 46(8), 78-82.
[13] Liu Y., & Chen L. (2019). Crop disease image recognition method based on transfer learning. Transactions of the Chinese Society for Agricultural Machinery, 50(8), 183-190.
[14] Goodfellow I., Bengio Y., & Courville A. (2016). Deep Learning. Cambridge: MIT Press.
[15] Sun Q., Wang K., & Bai X. (2018). Corn disease recognition based on CNN. Transactions of the Chinese Society for Agricultural Machinery, 49(5), 169-176.
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