Academic Journal
Comparison of machine learning and deep learning models for evaluating suitable areas for premium teas in Yunnan, China
| Title: | Comparison of machine learning and deep learning models for evaluating suitable areas for premium teas in Yunnan, China |
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| Authors: | Guiyu Wei, Ruliang Zhou |
| Source: | PLoS ONE, Vol 18, Iss 2 (2023) |
| Publisher Information: | Public Library of Science (PLoS), 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Medicine, Science |
| More Details: | Background: Tea is an important economic crop in Yunnan, and the market price of premium teas such as Lao Banzhang is significantly higher than ordinary teas. For planting lands to promote, the tea industry to develop and minority lands’ economies to prosper, it is vital to evaluate and analyze suitable areas for premium tea cultivation. Methods: Climate, terrain, soil, and green cropping system in the premium tea planting areas were used as evaluation variables. The suitability of six machine learning models for predicting suitable areas of premium teas were evaluated. Result: FA+ResNet demonstrated the best performance with an accuracy score of 0.94 and a macro-F1 score of 0.93. The suitable areas of premium teas were mainly located in the southern catchment of LancangJiang River, south-central part of Dehong, a few areas in the mid-west of Lincang, central scattered areas of Pu’er, most of the southern western part of Xishuangbanna and the southern edge of Honghe. Annual mean temperature, annual mean precipitation, mist belt, annual mean relative humidity, soil type and elevation were the key components in evaluating the suitable areas of premium teas in Yunnan. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1932-6203 |
| Relation: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9956044/?tool=EBI; https://doaj.org/toc/1932-6203 |
| Access URL: | https://doaj.org/article/25998df6385b40d6b9eaa78a22864964 |
| Accession Number: | edsdoj.25998df6385b40d6b9eaa78a22864964 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 19326203 |
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| Published in: | PLoS ONE |
| Language: | English |