AI - Driven Predictive modeling for Crop Optimization and Soil health Management – A Multilingual Integrated Framework
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Abstract
In the current era of this modern agricultural sector , the services like integrated automated , data – driven system is necessarily for optimizing the crop production and soil management . But , however the latest applications usually requires the small – scale farmers for advanced AI Analysis that required for smart decision for soil management and crop optimization to upload confidential farmer information to third – party cloud servers, that creates significant data leak privacy problem . While the previous agriculture study are relies on the historical datasets and old standard methods that usually get crop production right about 82% - 84% of the time . the ultimate objective is introduces a better , more securely system that improve those numbers while keeping the farm data confidential. It includes The Random Forest that captures localized data by combine the user inputs and soil values (N, P, K, pH) with its specific crop type and region and it fetches real-time weather data via through a live REST API . it hits 96.5% accuracy that it’s way better and great than the old 82% - 85% range . it gives the service of a chat assistant which is Grok AI that gives the result of the question that the user ask on the user device.
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References
] T. van Klompenburg et al., "Crop yield prediction using machine learning: A systematic literature review," Computers and Electronics in Agriculture, vol. 177, 2020.
[2] K. Palanivel and C. Surianarayanan, "An Approach for Prediction of Crop Yield Using Machine Learning and Big Data Techniques," IJCET, vol. 10, no. 3, 2019.
[3] Patel, M., Choudhary, N. (2017). Designing an Enhanced Simulation Module for Multimedia Transmission Over Wireless Standards. In: Modi, N., Verma, P., Trivedi, B. (eds) Proceedings of International Conference on Communication and Networks. Advances in Intelligent Systems and Computing, vol 508. Springer, Singapore. https://doi.org/10.1007/978-981-10-2750-5_17
[4] M. Champaneri et al., "Crop Yield Prediction Using Machine Learning," International Journal of Science and Research (IJSR), vol. 9, no. 4, 2020.
[5] Patel, Mayank, and Ruksar Sheikh. "Handwritten digit recognition using different dimensionality reduction techniques." International Journal of Recent Technology and Engineering 8.2 (2019): 999-1002.
[6] A. Crane-Droesch, "Machine learning methods for crop yield prediction and climate change impact assessment in agriculture," Environmental Research Letters, vol. 13, no. 11, 2018.
[7] Menaria, H.K., Nagar, P., Patel, M. (2020). Tweet Sentiment Classification by Semantic and Frequency Base Features Using Hybrid Classifier. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-15-0029-9_9
[8] A. Chlingaryan et al., "Machine learning approaches for crop yield prediction and nitrogen status estimation: A review," Computers and Electronics in Agriculture, vol. 151, 2018.