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

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 Naive 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.

Automated summary

Suitability evaluation of tea cultivation is crucial for improving yield and quality while ensuring sustainable development. This study developed a machine learning-based model to assess tea cultivation suitability at a town and village scale. By comparing different algorithms, 12 factors were selected and evaluated using the random forest algorithm. The results provide a scientific reference for land allocation decisions and sustainable agricultural development.