An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries.

ABSTRACT: Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the proces

SUBMITTER: Chang W 

PROVIDER: S-EPMC9961688 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature