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THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Harvest Forecasting Improvement Using Federated Learning and Ensemble Model

  • Received : 2023.10.20
  • Accepted : 2023.11.22
  • Published : 2023.11.30
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Abstract

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.

Keywords

Acknowledgement

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).

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