DOI QR코드

DOI QR Code

Harvest Forecasting Improvement Using Federated Learning and Ensemble Model

  • 투고 : 2023.10.20
  • 심사 : 2023.11.22
  • 발행 : 2023.11.30

초록

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

키워드

과제정보

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01489) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

참고문헌

  1. Saeed, K.; Lizhi, W.; "Crop yield prediction using deep neural networks," Frontiers in Plant Science, vol.10, Article 621, pp.1-10, May 2019  https://doi.org/10.3389/fpls.2019.00001
  2. N, B, Priya.; D, Tejasvi.; P, Vaishnavi.; "Crop yield prediction based on Indian agriculture using machine learning," International Journal of Modern Agriculture, vol.9, pp.1963-1973, Aug. 2020 
  3. Doshi, Z., Nadkarni, S., Agrawal, R., Shah, N., "AgroConsultant: intelligent crop recommendation system using machine learning algorithms," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-6, Aug. 2018. 
  4. Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., Khan, N., "A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction," IEEE Access, vol. 9, 63406-63439, Apr. 2021.  https://doi.org/10.1109/ACCESS.2021.3075159
  5. Saeed, K.; Lizhi, W.; Sotirios, V, A.; "A CNN-RNN framework for crop yield prediction," Frontiers in Plant Science, vol.10, Article 1750, pp.1-14, Jan. 2020  https://doi.org/10.3389/fpls.2019.01750
  6. Javad, A.; Lizhi, W.; Sotirios, V, A.; "An interaction regression model for crop yield prediction," Scientific Reports, Sep. 2021 
  7. Luke, B.; James, V, Z.; "Efficient stabilization of crop yield prediction in the Canadian Prairies," Elsevier, pp. 223-232, Sep. 2011 
  8. Bornn, L., Zidek, J. V., "Efficient stabilization of crop yield prediction in the Canadian Prairies," Agricultural and Forest Meteorology, vol. 152, 223-232, Jan. 2012.  https://doi.org/10.1016/j.agrformet.2011.09.013
  9. K. Pravallika; G. Karuna; K. Anuradha; V. Srilakshmi; "Deep Neural Network model for proficient crop yield prediction," E3S Web of Conferences 309, pp.1-10, 2021 
  10. Ashwini, I, P.; Ramesh, A, M.; Vinod, D.; "Crop yield prediction using machine learning techniques," International Journal of Scientific Research in Science, Engineering, and Technology, vol.7, no. 3, Mar. 2019 
  11. Zeel, D.; Subhash N.; Rashi A.; Neepa S.; "Agri Consultant: Intelligent Crop Recommendation System Using Machine Learning Algorithms," IEEE, Aug. 2018 
  12. HanByeol Oh; JongHyun Lim; SeungWeon Yang; YongYun Cho; ChangSun Shin; "A Study on the Prediction of Strawberry Production in Machine Learning Infrastructure," Smart Media Journal, Vol. 11, No. 5, pp. 9-16, Jun. 2022  https://doi.org/10.30693/SMJ.2022.11.5.9
  13. Jiuqing Dong; Alvaro Fuentes; Sook Yoon; Taehyun Kim; Dong Sun Park; "Towards Improved Performance on Plant Disease Recognition with Symptoms Specific Annotation," Smart Media Journal, Vol. 11, No. 4, pp. 38-45, May 2022  https://doi.org/10.30693/SMJ.2022.11.4.38
  14. Eunji Lee; Hyungwook Park; Eunju Kim; "A Study on LSTM-based water level prediction model and suitability evaluation," Smart Media Journal, Vol. 11, No. 5, pp. 56-62, Jun. 2022 https://doi.org/10.30693/SMJ.2022.11.5.56