Crop Recommendation and Production Prediction (original) (raw)

Abstract

Agriculture has always been a significant occupation across the world, and its products are essential to numerous industries, including clothing and food production. Therefore, our research aimed to predict crop types and yield using advanced machine learning models such as SVM, Random Forest, Decision Trees, and XGBoost. We considered several factors such as crop meteorological conditions, environmental factors, and rainfall to train our models. Our study produced remarkable findings that can help minimize losses in agriculture by selecting suitable crops for a particular land area. We employed two machine learning models in our research. In the first model, we identified the most suitable crops for a particular land area. Then, we used the output of the first model to train the second machine learning model. This second model predicts the crop yield percentage, helping farmers to identify the best crops that provide a high yield for a specific region based on user-provided parameters. Our research produced performance improvements over existing state-of-the-art methods in predicting crop types and yield. Our models demonstrated higher accuracy in identifying suitable crops and predicting crop yield. These findings have significant implications for the agriculture industry, helping farmers to increase production and financial stability while reducing the number of suicides in the agriculture sector. In conclusion, our research contributes to the development of advanced machine learning models for predicting crop types and yield, providing insights into sustainable agricultural practices.

Similar content being viewed by others

References

  1. Ranjani, J., Kalaiselvi, V.K.G., Sheela, A., Deepika Sree, D., Janaki, G.: Crop yıeld predıctıon usıng machıne learnıng algorıthm. IEEE (2021)
    Google Scholar
  2. Priyadharshini, K., Prabhavathi, R., Brindha Devi, V., Subha, P., Mohana Saranya, S., Kiruthika, K.: An enhanced approach for crop yıeld predıctıon system usıng lınear support vector machıne model. IEEE (2022)
    Google Scholar
  3. Jeong, J.H., et al.: Random forests for global and regional crop yield predictions. PLOS ONE 11(6), e0156571 (2016)
    Article PubMed PubMed Central Google Scholar
  4. Balakrishnan, N., Muthukumarasamy, G.: Crop production ensemble machine learning model for prediction. Int. J. Comput. Sci. Softw. Eng. 5, 148–153 (2016)
    Google Scholar
  5. Kumar, A., Kumar, N., Vats, V.: Effıcıent crop yıeld predıctıon usıng machıne learnıng algorıthms. Int. Res. J. Eng. Technol. 5, 3151–3159 (2018)
    Google Scholar
  6. Yesugade, K.D., Chudasama, H., Kharde, A., Mirashi, K., Muley, K.: Crop suggestıng system usıng unsupervısed machıne learnıng. Int. J. Comput. 7, 322–325 (2019)
    Google Scholar
  7. Sci. Eng. Open Access Res. Paper 7(3), (2019). E-ISSN: 2347-2693
    Google Scholar
  8. Khaki, S., Wang, L.: Crop yield prediction using deep neural networks. Front. Plant Sci. 10, 621 (2019). https://doi.org/10.3389/fpls.2019.00621
    Article PubMed PubMed Central Google Scholar
  9. Gandhi, N., Armstrong, L.J., Ptkar, R., Petkar, O., Kumar, A.: Rice crop yield prediction in India using support vector machines. In: International Joint Conference on Computer Science and Software Engineering. IEEE xplore (2016)
    Google Scholar
  10. Ghadge, R., Kulkarni, J., More, P., Nene, S., Priya, R.L.: Prediction of crop yield using machine learning. Int. Res. J. Eng. Technol. 5(2), 2237–2239 (2018)
    Google Scholar
  11. Suresh, A., Kumar, P.G., Ramalatha, M.: Prediction of major crop yields of Tamilnadu using K-means and Modified KNN. In 2018 3rd International Conference on Communication and Electronics Systems (ICCES), pp. 88–93. IEEE (2018)
    Google Scholar
  12. Shah, A., Dubey, A., Hemnani, V., Gala, D., Kalbande, D.R.: Smart farming system: crop yield prediction using regression techniques. In: Vasudevan, H., Deshmukh, A.A., Ray, K.P. (eds.) Proceedings of International Conference on Wireless Communication. LNDECT, vol. 19, pp. 49–56. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8339-6_6
    Chapter Google Scholar
  13. Patil, P., Panpatil, V., Kokate, S.: Crop prediction system using machine learning algorithms. Int. Res. J. Eng. Technol. 07(02) (2020)
    Google Scholar
  14. Thomas, K.T., Varsha, S., Saji, M.M., Varghese, L., Thomas, E.J.: Crop prediction using machine learning. Int. J. Future Gener. Commun. Netw. 13(3), 1896–1901 (2020)
    Google Scholar
  15. Dahikar, S.S., Rode, S.V.: Agricultural crop yield prediction using artificial neural network approach. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 2, 683–686 (2014)
    Google Scholar
  16. Bhosale, S.V., Thombare, R.A., Dhemey, P.G., Chaudhari, A.N.: Crop yield prediction using data analytics and hybrid approach. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5. IEEE (2018)
    Google Scholar
  17. Zingade, D.S., Buchade, O., Mehta, B., Ghodekar, S., Mehta, C.: Crop predıctıon system usıng machıne learnıng
    Google Scholar
  18. Suresh, G., Senthil Kumar, A., Lekashri, S., Manikandan, R.: Efficient crop yield recommendation system using machine learning for digital farming. Int. J. Modern Agric. 10(1), 906–914 (2021)
    Google Scholar
  19. India GDP sector-wise 2021 – StatisticsTimes.com
    Google Scholar
  20. Ray, S.: A quick review of machine learning algorithms. In: International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Com-IT-Con), India, 14th–16th Feb 2019
    Google Scholar
  21. Huang, Y., Lan, Y., Wang, F., Hu, Y., Liu, Y.: Prediction of maize yield using multi-source remote sensing data and machine learning algorithms. Remote Sens. 13(1), 117 (2021)
    Google Scholar
  22. Chen, X., Zhang, X., Hu, X.: Prediction of soybean yield using machine learning algorithms based on weather factors and plant population density. Agronomy 10(8), 1108 (2019)
    Google Scholar
  23. Huang, J., Li, X., Li, Z., Li, Y., Cao, Y.: Yield prediction for maize using machine learning algorithms. J. Appl. Remote Sens. (2019)
    Google Scholar
  24. Xia, X., Li, L., Zhang, M.: Yield prediction of maize using machine learning techniques based on weather data. Comput. Electron. Agric. (2017)
    Google Scholar
  25. Khan, F., Abdullah, M., Alghamdi, A.: Crop yield prediction using machine learning algorithms: a review. Comput. Electron. Agric. 157, 218–231 (2019)
    Google Scholar
  26. These references provide insights into the use of machine learning algorithms, remote sensing data, and weather factors for crop yield prediction
    Google Scholar

Download references

Acknowledgments

I express my sincere gratitude to my guide, Prof. Anupama C G, whose invaluable guidance and expertise has been instrumental in the successful completion of this study. I am also grateful to all those who have provided their support and assistance throughout the course of this work. Their contributions have been integral in the completion of this project.

Author information

Authors and Affiliations

  1. CINTEL, SRM Institute of Science and Technologies, Chennai, India
    C. G. Anupama, S. Selvakumara Samy, Harish Yarlagadda & Sunku Sai Nisvas Sankarsh

Authors

  1. C. G. Anupama
  2. S. Selvakumara Samy
  3. Harish Yarlagadda
  4. Sunku Sai Nisvas Sankarsh

Corresponding author

Correspondence toC. G. Anupama .

Editor information

Editors and Affiliations

  1. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Annie Uthra R.
  2. Department of Computer Technology, Anna University, Chennai, Tamil Nadu, India
    Kottilingam Kottursamy
  3. Department of Computer Technology, Anna University, Chennai, Tamil Nadu, India
    Gunasekaran Raja
  4. Manchester Metropolitan University, Manchester, UK
    Ali Kashif Bashir
  5. Department of Computer Engineering, Süleyman Demirel University, Isparta, Türkiye
    Utku Kose
  6. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Revathi Appavoo
  7. SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
    Vimaladevi Madhivanan

Rights and permissions

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper

Anupama, C.G., Samy, S.S., Yarlagadda, H., Sankarsh, S.S.N. (2024). Crop Recommendation and Production Prediction. In: R., A.U., et al. Deep Sciences for Computing and Communications. IconDeepCom 2023. Communications in Computer and Information Science, vol 2176. Springer, Cham. https://doi.org/10.1007/978-3-031-68905-5\_23

Download citation

Keywords

Publish with us