Sensors, Vol. 23, Pages 1811: An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries

Sensors, Vol. 23, Pages 1811: An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries Sensors doi: 10.3390/s23041811 Authors: Wenfeng Chang Xiao Wang Jing Yang Tao Qin 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 processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0....
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research