Extreme Gradient Boosting (XGBoost) Regressor and Shapley Additive Explanation for Crop Yield Prediction in Agriculture

Dennis A.L. Mariadass, Ervin Gubin Moung, Maisarah Mohd Sufian, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)


The primary purpose of precision agriculture is to maximize crop yields while utilizing a limited amount of land resources. Apart from industrialization, which fuelled Malaysia's significant economy and development, the country's agriculture industry performs a major role in guaranteeing food security and safety, as well as long-term development and wealth creation. To increase the nation's food security, policymakers must rely on accurate crop yield predictions in order to easily obtain trade - related evaluations. Machine Learning can help anticipate yields more accurately. This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia. Experiments on the generated yield dataset show promising results with 0.98 R-Squared value and outperformed the current models. The implementation of the suggested model is extensively evaluated using the Shapley Additive Explanation (SHAP) to discover the essential features such as average temperature, average rainfall, and pesticide in the crop yield prediction. The estimates provided by machine learning algorithms will aid farmers in deciding what to grow because of this research.

Original languageEnglish
Title of host publication2022 12th International Conference on Computer and Knowledge Engineering, ICCKE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665476133
ISBN (Print)9781665476140
Publication statusPublished - 29 Nov 2022
Externally publishedYes
Event12th International Conference on Computer and Knowledge Engineering - Mashhad, Iran, Islamic Republic of
Duration: 17 Nov 202218 Nov 2022
Conference number: 12

Publication series

NameInternational Conference on Computer and Knowledge Engineering
ISSN (Print)2375-1304
ISSN (Electronic)2643-279X


Conference12th International Conference on Computer and Knowledge Engineering
Abbreviated titleICCKE 2022
Country/TerritoryIran, Islamic Republic of
Internet address

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