An Efficient Modeling Based on XGBoost and SVM Algorithms to Predict Crop Yield-Reference-Cited by-同舟云学术

An Efficient Modeling Based on XGBoost and SVM Algorithms to Predict Crop Yield

Published:2022 Issue: Volume: Page:565-574
ISSN:2367-4512
Container-title:Advances in Data Science and Management
language:
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Author:

Mallikarjuna Rao G. S.,Dangeti Sujani,Amiripalli Shanmuk Srinivas

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-5685-9_55

Reference26 articles.

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3. Devika B, Ananthi B (2018) Analysis of crop yield prediction using data mining technique to predict annual yield of major crops. Int Res J Eng Technol 5(12):1460–1465

4. Amiripalli SS, Bobba V (2018) Research on network design and analysis of TGO topology. Int J Netw Virtual Organ 19(1):72–86

5. Amiripalli SS, Bobba V (2019) Trimet graph optimization (TGO) based methodology for scalability and survivability in wireless networks. Int J Adv Trends Comput Sci Eng 8(6):3454–3460

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