Home⇛Recoletos Multidisciplinary Research Journal⇛vol. 14 no. 1 (2026)

Smart Crop Selection and Irrigation Control Using Stacked Ensemble Learning

Vinay Kumar Enugala | Srinivas Prasad

Discipline: agricultural sciences

 

Abstract:

Background: Precision agriculture has continued to grow as modern farming uses data to manage resources and improve crop yields. This research aims to improve the efficiency of modern agricultural approaches by providing innovative data analysis for effective and sustainable irrigation management. Methods: The ensemble of a multi-layer perceptron (MLP) and an RF is performed using logistic regression as a learner. This ensemble model is used for crop prediction. The study encompasses various databases, including crop and nutrient mapping, sustainable irrigation datasets, and IoT sensor datasets with real-time records of soil moisture, temperature, and other relevant parameters. Results: The research achieved an ensemble model accuracy of 99. 34%, greater than the traditional technique in terms of precision and time efficiency. Conclusion: By leveraging IoT sensor data, this research improves the accuracy and proactivity of agricultural business practices and, therefore, supports the efficient use of agricultural resources.



References:

  1. khter, R., & Sofi, S. A. (2021). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University - Computer and Information Sciences, 34(8), 5602–5618. https://doi.org/10.1016/j.jksuci.2021.05.013
  2. Akkem, Y., Biswas, S. K., & Varanasi, A. (2023). Smart farming using artificial intelligence: A review. Engineering Applications of Artificial Intelligence, 120, 105899. https://doi.org/10.1016/j.engappai.2023.105899
  3. Alahmad, T., Neményi, M., & Nyéki, A. (2023). Applying IoT sensors and big data to improve precision crop production: A review. Agronomy, 13(10), 2603. https://doi.org/10.3390/agronomy13102603
  4. Bhimavarapu, U., Battineni, G., & Chintalapudi, N. (2023). Improved optimization algorithm in LSTM to predict crop yield. Computers, 12(1), 10. https://doi.org/10.3390/computers12010010
  5. Crawford, C. (2019). Crop nutrient database. Kaggle. https://www.kaggle.com/datasets/crawford/crop-nutrient-database
  6. Elavarasan, D., & Vincent, P. M. D. (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access, 8, 86886–86901. https://doi.org/10.1109/access.2020.2992480
  7. European Space Agency. (n.d.). RapidEye ESA archive. https://earth.esa.int/eogateway/catalog/rapideye-esa-archive
  8. Farmonov, N., Amankulova, K., Szatmári, J., Sharifi, A., Abbasi‐Moghadam, D., Mahdi, S., & Mucsi, L. (2023). Crop type classification by DESIS hyperspectral imagery and machine learning algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 1576–1588. https://doi.org/10.1109/jstars.2023.3239756
  9. Harshil. (2020). Intelligent irrigation system. Kaggle. https://www.kaggle.com/datasets/harshilpatel355/autoirrigationdata
  10. Ikram, A., Aslam, W., Aziz, R. H. H., Noor, F., Mallah, G. A., Ikram, S., Ahmad, M. S., Abdullah, A. M., & Ullah, I. (2022). Crop yield maximization using an IoT-Based smart decision. Journal of Sensors, 2022(1). https://doi.org/10.1155/2022/2022923
  11. Jhajharia, K., Mathur, P., Jain, S., & Nijhawan, S. (2023). Crop yield prediction using machine learning and deep learning techniques. Procedia Computer Science, 218, 406–417. https://doi.org/10.1016/j.procs.2023.01.023
  12. Kayum, A., & Rahman, M. O. (2023). Appropriate crop recommended system for cultivation using IoT and ML. 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI). https://doi.org/10.1109/sti59863.2023.10464865
  13. Keerthana, M., Meghana, K. J. M., Pravallika, S., & Kavitha, M. (2021). An ensemble algorithm for crop yield prediction. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 963–970. https://doi.org/10.1109/icicv50876.2021.9388479
  14. Kiruthika, S., & Karthika, D. (2023). IOT-BASED professional crop recommendation system using a weight-based long-term memory approach. Measurement: Sensors, 27, 100722. https://doi.org/10.1016/j.measen.2023.100722
  15. Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., & Uwamahoro, A. (2023). Crop yield prediction using machine learning models: Case of Irish potato and maize. Agriculture, 13(1), 225. https://doi.org/10.3390/agriculture13010225
  16. Morales, A. G., & Villalobos, F. J. (2023). Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1128388
  17. Nikhil, U. V., Pandiyan, A. M., Raja, S. P., & Stamenkovic, Z. (2024). Machine learning-based crop yield prediction in south india: Performance analysis of various models. Computers, 13(6), 137. https://doi.org/10.3390/computers13060137
  18. Oikonomidis, A., Catal, C., & Kassahun, A. (2022). Hybrid deep learning-based models for crop yield prediction. Applied Artificial Intelligence: An International Journal, 36(1), 1–18. https://doi.org/10.1080/08839514.2022.2031823
  19. Priyadharshini, A., Chakraborty, S., Kumar, A., & Pooniwala, O. R. (2021). Intelligent crop recommendation system using machine learning. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 843–848. https://doi.org/10.1109/ICCMC51019.2021.9418375
  20. Rani, S., Mishra, A. K., Kataria, A., Mallik, S., & Qin, H. (2023). Machine learning-based optimal crop selection system in smart agriculture. Scientific Reports, 13(1), 15997. https://doi.org/10.1038/s41598-023-42356-y
  21. Sharma, P., Dadheech, P., Aneja, N., & Aneja, S. (2023). Predicting agriculture yields based on machine learning using regression and deep learning. IEEE Access, 11, 111255–111264. https://doi.org/10.1109/access.2023.3321861
  22. Talaat, F. M. (2023). Crop yield prediction algorithm (CYPA) in precision agriculture based on IoT techniques and climate changes. Neural Computing & Applications, 35(23), 17281–17292. https://doi.org/10.1007/s00521-023-08619-5
  23. Vignesh, K., Askarunisa, A., & M. Abirami, A. (2023). Optimized deep learning methods for crop yield prediction. Computer Systems Science and Engineering, 44(2), 1051–1067. https://doi.org/10.32604/csse.2023.024475

Tools


Copyright © 2026  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic