Comparison of machine learning and deep learning models for evaluating suitable areas for premium teas in Yunnan, China

Ru-liang, Zhou

doi:10.1371/journal.pone.0282105

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Research in this domain highlights the adeptness of deep learning in capturing intricate nonlinear relationships between evaluation factors and suitability levels, thereby mitigating human errors in data processing 5 , 36 , 37 . Wei et al 38 assessed the applicability of multiple AI models for predicting suitable areas for agricultural zones in Yunnan Province. The study compared the performance of Support Vector Machines (SVM), k-Nearest Neighbor (kNN), Back Propagation Neural Network (BPNN), Convolutional Neural Networks (CNN), and the The Feature Attention (FA) + Residual Neural Network (ResNet) model in the multi-class prediction of agricultural suitability zones in Yunnan Province.…”

Section: Introductionmentioning

confidence: 99%

Evaluation method for ecology-agriculture-urban spaces based on deep learning

Li,

Zhang,

Hong

et al. 2024

Sci Rep

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With the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for fostering sustainable development. This study focuses on assessing the "Ecology-Agriculture-Urban" (E-A-U) space in Yulin City, China, as a representative case. Following the framework proposed by Chinese named "environmental capacity and national space development suitability evaluation" (hereinafter referred to as "Double Evaluation"), we developed a Self-Attention Residual Neural Network (SARes-NET) model to assess the E-U-A space. Spatially, the northwest region is dominated by agriculture, while the southeast is characterized by urban and ecological areas, aligning with regional development patterns. Comparative validations with five other models, including Logistic Regression (LR), Naive Bayes (NB), Gradient Boosting Decision Trees (GBDT), Random Forest (RF) and Artificial Neural Network (ANN), reveal that the SARes-NET model exhibits superior simulation performance, highlighting it’s ability to capture intricate non-linear relationships and reduce human errors in data processing. This study establishes deep learning-guided E-A-U spatial evaluation as an innovative approach for national spatial planning, holding broader implications for national-level territorial assessments.

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Section: Introductionmentioning

confidence: 99%

Evaluation method for ecology-agriculture-urban spaces based on deep learning

Li,

Zhang,

Hong

et al. 2024

Sci Rep

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“…Along with socioeconomic development, tea garden development plays a critical role in attaining self-sufficiency for the dependent population [3]. of the evaluation variables, climate, terrain, and green-cropping system variables were used [21]. Six machine-learning models were used in the study.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Tea Plantation Suitability Using Geostatistical and Machine Learning Techniques: A Case of Darjeeling Himalaya, India

et al. 2023

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This study aimed to identify suitable sites for tea cultivation using both random forest and logistic regression models. The study utilized 2770 sample points to map the tea plantation suitability zones (TPSZs), considering 12 important conditioning factors, such as temperature, rainfall, elevation, slope, soil depth, soil drainability, soil electrical conductivity, base saturation, soil texture, soil pH, the normalized difference vegetation index (NDVI), and land use land cover (LULC). The data were normalized using ArcGIS 10.2 and the models were calibrated using 70% of the total data, while the remaining 30% of the data were used for validation. The final TPSZ map was classified into four different categories: highly suitable zones, moderately suitable zones, marginally suitable zones, and not-suitable zones. The study revealed that the random forest (RF) model was more precise than the logistic regression model, with areas under the curve (AUCs) of 85.2% and 83.3%, respectively. The results indicated that well-drained soil with a pH range between 5.6 and 6.0 is ideal for tea farming, highlighting the importance of climate and soil properties in tea cultivation. Furthermore, the study emphasized the need to balance economic and environmental considerations when considering tea plantation expansion. The findings of this study provide important insights into tea cultivation site selection and can aid tea farmers, policymakers, and other stakeholders in making informed decisions regarding tea plantation expansion.

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“…Yunnan Province, known for its rich biodiversity and ethnic diversity, has a profound tea culture and is globally recognized for its unique Pu'er tea (Wei & Zhou, 2023). Specifically, Yaoqu Township in Yunnan is characterized by a strong presence of ethnic minority women actively engaged in the tea industry.…”

Section: Introductionmentioning

confidence: 99%

Barriers and Opportunities for Women in Tea Tourism: A Qualitative Examination of Policy Context in Yaoqu Township, China

2023

JCPP

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This study seeks to: (1) investigate the current state of women's participation in tea tourism in Yaoqu Township, Yunnan Province, China; (2) explore the impacts of local and national policies on women's participation in tea tourism; (3) identify barriers and opportunities for women in the tea tourism industry within the policy context. This is achieved by conducting an indepth qualitative analysis of existing policy context. There are 12 important stakeholders, including local women in the tea and tourist industries, township committees, tourism officials, tea plantation owners, and tourists were interviewed using a semi-structured format. Preliminary discoveries indicate that despite the existence of policies aimed at promoting the growth of tourism and empowering women, their successful execution continues to pose a formidable obstacle. Gaps emerge between policy and practice, largely due to lack of awareness, inadequate training, and gendered socio-cultural norms. These gaps inadvertently lead to missed opportunities for women in tea tourism. The study underscores the need for context-specific, gender-responsive policy interventions that promote equal opportunities, capacity building, and empowerment of women in the burgeoning tea tourism industry. This research holds value for policymakers, tourism developers, and gender advocates, providing empirical insights that may guide policy refinement and targeted interventions, driving equitable and sustainable development in regions similar to Yaoqu Township.

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Comparison of machine learning and deep learning models for evaluating suitable areas for premium teas in Yunnan, China

Cited by 4 publications

References 24 publications

Evaluation method for ecology-agriculture-urban spaces based on deep learning

Evaluation method for ecology-agriculture-urban spaces based on deep learning

Analysis of Tea Plantation Suitability Using Geostatistical and Machine Learning Techniques: A Case of Darjeeling Himalaya, India

Barriers and Opportunities for Women in Tea Tourism: A Qualitative Examination of Policy Context in Yaoqu Township, China

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