Harvest Forecasting Improvement Using Federated Learning and Ensemble Model

• 영문명: Harvest Forecasting Improvement Using Federated Learning and Ensemble Model
• 발행기관: 한국스마트미디어학회
• 저자명: Ohnmar Khin 고진광 이성근
• 간행물 정보: 『스마트미디어저널』Vol12, No.10, 9~18쪽, 전체 10쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2023.11.30

영문 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.

목차

I. INTRODUCTION

II. DATA AND FEATURES

III. MATERIALS AND METHODS

IV. RESULTS AND DISCUSSION

V. CONCLUSION

REFERENCES

키워드

Harvest forecasting Federated learning Ensemble algorithms Multiple aspects