AI-Driven Big Data Analytics for Crop Disease Prediction in Smart Agriculture
Keywords:
Crop diseases, artificial intelligence, big data analytics, smart agriculture, remote sensing, Internet of Things.Abstract
The conclusion highlights that crop disease prediction involves the fusion of different data sources, including field monitoring data, satellite imagery, and historical disease information. A Web-based, end-to-end solution is proposed to facilitate easy access and utilization of crop disease prediction models by researchers, farmers, and organizations. Future trends include monitoring and assessing the effects of climate change on crop disease, formulating preventive measures, and integrating remotely sensed data to enable foresighted, AI-enhanced analyses.
Early and accurate prediction of crop diseases can greatly minimize loss of yield, effort, and resources. Disease prediction considers multiple data sources, such as monitoring systems that capture temperature, humidity, soil moisture, light, rainfall, and disease episodes, and satellite images that help in monitoring disease outbreaks. Machine learning and deep learning methods are widely used for disease prediction; however, the models require periodic retraining for long-term effectiveness. Key steps include feature engineering, feature selection, training and validation of different ML and DL models, confusion matrix analysis, and performance assessment using metrics such as accuracy, precision, recall, F1 score, and AUC. An end-to-end system architecture addresses data pipelines, storage, and model-building and monitoring.
References
1. Zhou, J., Li, J., Wang, C., Wu, H., Zhao, C., & Teng, G. (2021). Crop disease identification and interpretation method based on multimodal deep learning. Computers and Electronics in Agriculture, 189, 106408.
2. Abbas, A., Jain, S., Gour, M., & Vankudothu, S. (2021). Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and Electronics in Agriculture, 187, 106279.
3. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
4. Gui, P., Dang, W., Zhu, F., & Zhao, Q. (2021). Towards automatic field plant disease recognition. Computers and Electronics in Agriculture, 191, 106523.
5. Canals, R., et al. (2021). Computer vision, IoT and data fusion for crop disease detection using machine learning: A survey and ongoing research. Remote Sensing, 13(13), 2486.
6. Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.
7. Albattah, W., Nawaz, M., Javed, A., Masood, M., & Albahli, S. (2022). A novel deep learning method for detection and classification of plant diseases. Complex & Intelligent Systems, 8, 507–524.
8. Borhani, Y., Khoramdel, J., & Najafi, E. (2022). A deep learning based approach for automated plant disease classification using vision transformer. Scientific Reports, 12, 11554.
9. Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
10. Nawaz, M., Nazir, T., Javed, A., Masood, M., Rashid, J., Kim, J., & Hussain, A. (2022). A robust deep learning approach for tomato plant leaf disease localization and classification. Scientific Reports, 12, 18568.
11. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
12. Chen, W., Chen, J., Duan, R., Fang, Y., Ruan, Q., & Zhang, D. (2022). MS-DNet: A mobile neural network for plant disease identification. Computers and Electronics in Agriculture, 199, 107175.
13. Janarthan, S., Thuseethan, S., Rajasegarar, S., & Yearwood, J. (2022). P2OP—Plant Pathology on Palms: A deep learning-based mobile solution for in-field plant disease detection. Computers and Electronics in Agriculture, 202, 107371.
14. Inala, R. (2021). A New Paradigm in Retirement Solution Platforms: Leveraging Data Governance to Build AI-Ready Data Products. Journal of International Crisis and Risk Communication Research, 286-310.
15. Dey, B., Haque, M. M. U., Khatun, R., & Ahmed, R. (2022). Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and NPK deficiency symptoms of rice (Oryza sativa). Computers and Electronics in Agriculture, 202, 107340.
16. Inala, R. (2022). Engineering Data Products for Investment Analytics: The Role of Product Master Data and Scalable Big Data Solutions. International Journal of Scientific Research and Modern Technology, 155-171.
17. Saleem, M. H., Potgieter, J., & Arif, K. M. (2022). Plant disease detection and classification by deep learning. Plants, 11(6), 1–24.
18. Li, Z., Yang, J., Wu, H., & Zhang, Y. (2022). Deep learning for plant disease recognition: A comprehensive review. Agriculture, 12, 1350.
19. Aravind, R., Deon, E., & Surabhi, S. N. R. D. (2024). Developing cost-effective solutions for autonomous vehicle software testing using simulated environments using AI techniques. Educational Administration: Theory and Practice, 30(6), 4135-4147.
20. Alzahrani, M. S., Alotaibi, M. A., & Alharbi, A. (2022). Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey. Agriculture, 12(9), 1350.
21. Jung, M., Song, J. S., Shin, A. Y., Choi, B., Go, S., Kwon, S.-Y., Park, J., Park, S. G., & Kim, Y.-M. (2023). Construction of deep learning-based disease detection model in plants. Scientific Reports, 13, 7331.
22. Reddy, V. A. R. (2024). Generative Intelligence for Healthcare Claims Processing and Personalized Benefits Management. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14099.
23. Shi, T., Liu, Y., Zheng, X., Hu, K., Huang, H., Liu, H., & Huang, H. (2023). Recent advances in plant disease severity assessment using convolutional neural networks. Scientific Reports, 13, 2336.
24. Salman, Z., Muhammad, A., Piran, M. J., & Han, D. (2023). Crop-saving with AI: Latest trends in deep learning techniques for plant pathology. Frontiers in Plant Science, 14, 1224709.
25. Aravind, R., & Surabhi, S. N. R. D. (2024). Smart charging: AI solutions for efficient battery power management in automotive applications. Educational Administration: Theory and Practice, 30(5), 14257-1467.
26. Shahoveisi, F., Taheri Gorji, H., Shahabi, S., Hosseinirad, S., Markell, S., & Vasefi, F. (2023). Application of image processing and transfer learning for the detection of rust disease. Scientific Reports, 13, 5133.
27. Luo, Y., Cai, X., Qi, J., Guo, D., & Che, W. (2023). FPGA-accelerated CNN for real-time plant disease identification. Computers and Electronics in Agriculture, 207, 107715.
28. Dey, N., et al. (2023). Automation of crop disease detection through conventional machine learning and deep transfer learning approaches. Agriculture, 13(2), 352.
29. Aitha, A. R. (2024). Generative AI-Powered Fraud Detection in Workers' Compensation: A DevOps-Based Multi-Cloud Architecture Leveraging, Deep Learning, and Explainable AI. Computer Fraud and Security.
30. Hassan, S. M., et al. (2023). DeepCrop: Deep learning-based crop disease prediction with web application. Journal of Agriculture and Food Research, 14, 100764.
31. Jafar, A., Bibi, N., Naqvi, R. A., Sadeghi-Niaraki, A., & Jeong, D. (2024). Revolutionizing agriculture with artificial intelligence: Plant disease detection methods, applications, and their limitations. Frontiers in Plant Science, 15, 1356260.
32. Natarajan, S., Chakrabarti, P., & Margala, M. (2024). Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI. Scientific Reports, 14, 13695.
33. Aravind, R. (2024). Integrating controller area network (CAN) with cloud-based data storage solutions for improved vehicle diagnostics using AI. Educational Administration: Theory and Practice, 30(1), 992-1005.
34. Yang, B., Li, M., Li, F., Wang, Y., Liang, Q., Zhao, R., Li, C., & Wang, J. (2024). A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method. Scientific Reports, 14, 11664.
35. Rezaei, M., Diepeveen, D., Laga, H., Jones, M. G. K., & Sohel, F. (2024). Plant disease recognition in a low data scenario using few-shot learning. Computers and Electronics in Agriculture, 219, 108812.
36. Wang, H., Nguyen, T. H., Nguyen, T. N., & Dang, M. (2024). PD-TR: End-to-end plant diseases detection using a transformer. Computers and Electronics in Agriculture, 224, 109123.
