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Original Article
Smart Crop Advisory Systems Using Artificial Intelligence and Machine Learning
Sneh Lata Singh1
Amit Yadav2
Shanu Ahmed3
Abhay Maurya4
Varun Rana5
Samir Ahmad6
1 Assistant professor, Department of Computer Science and Engineering, Dr. A.P.J Abdul Kalam Institute of Technology, Tanakpur, Champawat, Uttarakhand, India. 2 3 4 5 6 Department of Computer Science and Engineering, Dr.A.P.J Abdul Kalam Institute of Technology, Tanakpur Champawat, Uttarakhand, India.
Published Online: September-December 2025
Pages: 269-275
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403043References
1. Pawan, D. Yadav, R. K. Sharma, M. Kumar, J. Rani, and N. Sharma, “An effective approach for crop recommendation using features of
specific locations and seasons to maximize crop yield production using machine learning,” International Journal of Intelligent Systems and
Applications in Engineering, vol. 12, no. 18s, pp. 844–850, 2024.
2. G. Mamatha and J. S. Nayak, “A novel design and implementation of fertilizer recommendation system based on hybrid machinelearning models,” SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 11, pp. 448–460, 2024.
doi:10.14445/23488379/IJEEE-V11111P141
3. A. Chaudhary, M. Gupta, and U. Tiwari, “Crop disease detection using deep learning models,” in Proceedings of the 2023 IEEE International
Conference on Recent Advances in Systems Science and Engineering (RASSE), IEEE, 2023.
4. A. A. Ingole and N. E. Karale, “Java in microservices architecture: A study on Spring Boot and cloud-native development,” International
Journal of Ingenious Research, Invention and Development, vol. 4, no. 2, 2025.
5. A. Murad, “Comparative performance and user experience: PWAs vs. native mobile applications,” in Proceedings of the 11th International
Scientific and Practical Conference on Current Issues and Prospects for the Development of Scientific Research, Orléans, France,pp.
246–260,2025. doi: 10.51582/interconf.19-20.08.2025.027
6. Kaggle, “Agricultural datasets for crop, fertilizer, and disease prediction,” 2024. [Online]. Available: https://www.kaggle.com
7. National Bank for Agriculture and Rural Development (NABARD), Annual Report, NABARD, India, 2022.
8. Open Weather, “Open Weather API documentation,” 2024. [Online]. Available: https://openweathermap.org/ api
9. Python Software Foundation, “Python documentation,”2024.[Online]. Available: https://docs.python.org/3/
10. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
specific locations and seasons to maximize crop yield production using machine learning,” International Journal of Intelligent Systems and
Applications in Engineering, vol. 12, no. 18s, pp. 844–850, 2024.
2. G. Mamatha and J. S. Nayak, “A novel design and implementation of fertilizer recommendation system based on hybrid machinelearning models,” SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 11, pp. 448–460, 2024.
doi:10.14445/23488379/IJEEE-V11111P141
3. A. Chaudhary, M. Gupta, and U. Tiwari, “Crop disease detection using deep learning models,” in Proceedings of the 2023 IEEE International
Conference on Recent Advances in Systems Science and Engineering (RASSE), IEEE, 2023.
4. A. A. Ingole and N. E. Karale, “Java in microservices architecture: A study on Spring Boot and cloud-native development,” International
Journal of Ingenious Research, Invention and Development, vol. 4, no. 2, 2025.
5. A. Murad, “Comparative performance and user experience: PWAs vs. native mobile applications,” in Proceedings of the 11th International
Scientific and Practical Conference on Current Issues and Prospects for the Development of Scientific Research, Orléans, France,pp.
246–260,2025. doi: 10.51582/interconf.19-20.08.2025.027
6. Kaggle, “Agricultural datasets for crop, fertilizer, and disease prediction,” 2024. [Online]. Available: https://www.kaggle.com
7. National Bank for Agriculture and Rural Development (NABARD), Annual Report, NABARD, India, 2022.
8. Open Weather, “Open Weather API documentation,” 2024. [Online]. Available: https://openweathermap.org/ api
9. Python Software Foundation, “Python documentation,”2024.[Online]. Available: https://docs.python.org/3/
10. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
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