Suitability Evaluation of Tea Cultivation Using Machine Learning Technique at Town and Village Scales

Abstract

Suitability evaluation of tea cultivation is very important for improving the yield and quality of tea, which can avoid blind expansion and achieve sustainable development; however, to date, relevant research at town and village scales is lacking. This study selected Xinming Township in Huangshan City, Anhui Province, as the study area, which is the main production area of Taiping Houkui Tea—one of the ten most famous teas in China. We proposed a machine learning-based tea cultivation suitability evaluation model by comparing logistic regression (LR), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), random forest (RF), Gaussian Naïve Bayes (GNB), and multilayer perceptron (MLP) to calculate the weight accuracy of the evaluation factors. We then selected 12 factors, including climate, soil, terrain, and ecological economy factors, using the RF with the highest accuracy to calculate the evaluation factor weights and obtained the suitability evaluation results. The results show that the highly suitable area, moderately suitable area, generally suitable area, and unsuitable area land categories for tea cultivation were 14.13%, 27.25%, 32.46%, and 26.16%, respectively. Combined with field research, the highly suitable areas were mainly distributed in northwest Xinming Town, which is in line with the distribution of tea cultivation at the Xinming township level. The results provide a scientific reference to support land allocation decisions for tea cultivation and sustainable green agricultural development at the town and village scales.