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Article

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

by
Wenwen **ng
1,
Cheng Zhou
1,
Junli Li
1,2,*,
Weiyin Wang
1,
**gchi He
1,
Youjun Tu
1,
**u Cao
1 and
Yunhua Zhang
1
1
Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, Anhui Agricultural University, Hefei 230036, China
2
College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(9), 2010; https://doi.org/10.3390/agronomy12092010
Submission received: 16 July 2022 / Revised: 20 August 2022 / Accepted: 21 August 2022 / Published: 25 August 2022
(This article belongs to the Special Issue Environmental Ecological Remediation and Farming Sustainability)
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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 ** 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 **nming Town, which is in line with the distribution of tea cultivation at the **nming 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.

1. Introduction

Tea is a woody plant belonging to the angiosperm family of vegetation classification system [1]. External morphology is influenced by external environmental conditions and various branching habits, which result in plant forms bearing different types of tea leaves. China is the home of tea. China’s tea production increased from 0.68 million tons in 2000 to 2.93 million tons in 2020. Although consumption of tea was somewhat affected by the COVID-19 pandemic in 2020, the market value of tea remains high, as much as 2.038 billion dollars [2]. Tea is an integral part of the lives of Chinese people, especially in the Anhui, Yunnan, Guizhou, Sichuan, Zhejiang, Hubei, and Fujian provinces [3]. Tea has become one of the main cash crops in these regions and a leading industry in some of the villages and towns in these regions. The quality and yield of tea depends on a number of characteristics of the cultivation area, thus constraining the further development of the tea industry [4,5]. Therefore, it is necessary to explore tea cultivation suitability to address the challenge of tea yield reduction and low quality caused by the lack of scientific or reasonable planning in the early stages of tea plantation construction, as well as to avoid increasing fertilizer, pesticides and other agricultural chemical applications, which mitigate agricultural source pollution [6]. Additionally, tea cultivation suitability research can provide information regarding the constraints of land use for tea, which is important for the high quality and high yield of tea, the development of the tea industry, and the efficient use of agricultural land resources.
In recent years, several relevant studies have been conducted on the suitability evaluation of crops, such as tea. These studies are mainly divided into evaluation factors, evaluation methods, and evaluation scales. The evaluation factors are mainly classified into natural environmental factors and anthropogenic factors [7]. Natural environmental factors include climate, soil, and topography. Further, average temperature, maximum temperature, minimum temperature, accumulated temperature, evaporation, relative humidity, precipitation, sunshine duration, and wind speed are the characteristics of climate factors [8,9,10]. Soil characteristics include soil pH, available phosphorus, available potassium, ammonium, and nitrate [11,12,13]. The topography characteristics are mainly slope, aspect, altitude, and elevation, among others [14,15]. Hence, natural factors are essential for the evaluation of crop cultivation suitability. Anthropogenic factors mainly involve distance from roads and rivers, transportation costs, drainage, population density, and per capita GDP [16,17,18]; some evaluation models mainly reflect the impact of anthropogenic activities on the cultivation of crops, such as tea, and the labor cost in the process of tea cultivation. Existing research has begun to combine natural and anthropogenic factors to carry out a comprehensive evaluation that provides a more scientific theoretical basis for tea production decisions.
The evaluation methodologies can be divided into subjective weighting methods, objective weighting methods, and subjective–objective weighting methods. Subjective weighting is the earliest method based on the importance of decision makers in assigning different weights to the evaluation factors. Although this method is simple and widely used, it has many limitations in its application because of its subjective and arbitrary nature, including the analytic hierarchy process [19], fuzzy analysis [20], and expert scoring method [21], among others. Objective weighting uses the relationship between the evaluation factors and the interaction between the results and evaluation factors to determine the weight, wherein mathematical theory is strong, but its universality is poor, and the calculation is cumbersome; these include the entropy method [22] and principal component analysis [23], among others. With the development of information technology, machine learning theory and methods have been introduced into suitability evaluation systems, such as artificial neural network (ANN) [24], maximum entropy model (MaxEnt) [25], Bayesian [26], random forest (RF) [27], regression model [28], and deep learning [29], with a collinear relationship between evaluation factors. Some machine learning methods also have overfitting and weak interpretability to prevent the implicit superposition of weights in the process of factor weighting; these include the analytic hierarchy process and gray correlation [30], fuzzy mathematics [3], genetic algorithm [31], and other methods. These methods achieved good results.
At the evaluation scale, the existing crop suitability evaluation system for tea and other crops is mostly aimed at the national, provincial, municipal, and county macroscale. Most tea suitability evaluations are based on multi-standard evaluations of China, India, Kenya, and Sri Lanka—the four major tea-producing countries [9,10,32]. Some studies have focused on tea production areas in different provinces, such as Lahijan in Iran [33], Yingde in Guangdong, Zhejiang and Yunnan Province in China [3,7,34]. However, it is difficult to obtain evaluation data on a macro scale. The number of selected factors is not comprehensive, and the accuracy of the natural factor sampling points is poor; therefore, the evaluation results cannot be accurate for specific areas. In recent times, studies have been conducted on suitability evaluations of tea and other crops in cities and counties as the research areas, such as the Yuyao and Shangnan Counties or the Shaanxi Province [35,36]. Compared with large scales such as national and municipal scales, town and village scales could reveal detailed characteristics of the spatial distribution of tea.
As the demand for tea continues to grow both in China and internationally, the area under cultivation and the total yield of tea are expected to increase accordingly. However, given that the total area of suitable land is limited, it is of the utmost importance to develop a plan for further development of the tea sector in important tea-producing regions. In this study, we considered ** Houkui tea—one of the ten most famous teas. Its planting area is mainly distributed in the Fenghuangshan, Shitongshan, Jigongshan, and Jigongjian areas of the three natural villages of Houkeng (Figure 2), Hougang, and Yanjia in Sanhe Village of ** Houkui Tea”.

2.2. Tea Cultivation Suitability Evaluation

Tea cultivation suitability evaluation is often referred to as one of the multi-criteria evaluations of crop cultivation, which analyzes the suitability of regional planting by selecting influencing factors and calculating factor weights in accordance with the needs of tea growth. The suitability evaluation of tea cultivation is beneficial to the scientific planning of tea plantations and the sustainable development of the tea industry [5]. Meanwhile, in the fields of wheat, rice, coffee, and other crops, the cultivation suitability is commonly used [26,37]. However, the analytic hierarchy process is mostly used in the conventional planting suitability evaluation model, which has subjective factors interfering. Therefore, researchers have increased the objectivity of evaluation results by using objective evaluation techniques such as machine learning. As a result, the present suitability evaluation can be broadly divided into three types based on the methodologies now in use: subjective evaluation methods, objective evaluation methods, and subjective and objective evaluation methods combined.

2.3. Data

The Digital Elevation Model (DEM) used in this study was obtained from the Geospatial Data Cloud System of the Computer Network Information Center of the Chinese Academy of Sciences. Available online: http://www.gscloud.cn (accessed on 15 October 2020). And we use a GDEMDEM 30 m resolution to analyze slope and aspect. Meteorological data sources were combined with those of the Meteorological Bureau of Huangshan City. As there is no weather station in ** Houkui in ** Houkui Tea in **nxiang City was mapped.

4. Results

4.1. Evaluation Unit Factor

We used the ordinary kriging method in geostatistical analysis to interpolate the selected evaluation factors, which are widely used in crop suitability evaluation, visualization of soil nutrients, heavy metals, and meteorological spatial variation [60,61]. The processed data was reclassified to complete the data preprocessing. The results of the processing are shown in Figure 4a–l, with spatially interpolated evaluation factors.
The spatial distribution of the evaluation factors selected in this study revealed that the suitable areas were mostly concentrated in the central region, which is located at the intersection of the three villages and towns of Monkey Hang Village, Qiaoshan Village, and **nming Village. Among them, the most suitable tea growing area, considering pH as the highest factor of importance, was found to be mainly distributed in the western part of Monkey Hang and the Zhaotao Village, the northern part of Gehu Village, and the northern part of Qiaoshan Village. With average temperature as an important factor, less suitable areas were mainly concentrated in the eastern and western part of the Gehu Village, while other villages and towns were found to be more suitable for the growth of tea trees. The most suitable tea growing area according to relative humidity factor was mainly distributed in the Gehu Village and the southern part of Monkey Hang. The content of fast-acting potassium in the soil of **nming Township was in line with the growth of tea trees. The most suitable area according to nitrogen content was the south of Monkey Hang and the north of Zhaotao Village. The suitable area based on slope showed irregular space distribution and was distributed in all villages. The suitable area based on altitude was mainly distributed in the south of Qiaoshan Village and the east of Gehu Village, though the altitude also showed irregular distribution in terms of space. The weights of the other evaluation factors were not greater than 0.3, and therefore had less influence on the results of tea growth.

4.2. Comparison of Machine Learning Methods

Comprehensive evaluation experiments were conducted using seven machine learning methods. To compare the final results, receiver operating characteristic (ROC) curves were used to test the accuracy of the prediction models. The ROC curve is a common model validation method in applicability evaluation systems that are not subject to critical constraints. In addition, the specificity and sensitivity were calculated by continuously changing the judgment threshold. The area enclosed by the ROC curve and marker line is called the area under the curve (AUC) value. The higher the AUC value, the higher the accuracy of the model [62]. The AUC values calculated in this study are shown in Figure 5. For LR, RF, XGBoost, AdaBoost, GBDT, GNB, and MLP, the calculated AUC values are 0.618, 0.86, 0.819, 0.731, 0.853, 0.566, and 0.539, respectively. From the perspective of evaluation accuracy, the highest AUC value was calculated by the RF method, indicating its feasible application in the evaluation system of tea cultivation suitability, with better calculation accuracy. Therefore, this method was used in this study to calculate the weights of evaluation factors.

4.3. Factor Weight Calculation

The idea of evaluating the importance of evaluation factors in RF is based on the average contribution of each evaluation factor to each tree in RF. There are generally two methods to measure the contribution: the Gini index or the OOB error rate. In this study, the Gini index was used as the evaluation standard to measure contribution [63], and the calculation formula is as follows:
G I = 1 − ∑ n = 1 n p n m 2
where G I is the Gini index, n is the category, m is the node, and pnm is the proportion of n in m. Using this formula, two features randomly extracted from node m were assigned different probabilities.
The importance of feature a i in node m is denoted by V a , and its Gini index before and after node m is inconsistent. Therefore, the change in the Gini index of a i in node m is V a i = G I − G I a 1 − G I a 2 (GIa1 is the Gini index before the node, and GIa2 is the Gini index after the node). Finally, the importance of the evaluation factor was normalized [63], and the equation is as follows:
V = V j ∑ i = 1 c V i
Each evaluation factor was substituted into formulas (1) and (2) to calculate the weights of each evaluation factor (Table 3). Referring to studies of Chen [7], Das [16], and other researchers [15], and combining these results with the data in the present study, we classified the factors into different levels (Table 3). The largest weight value of 0.132 is accounted for by pH, followed by average temperature and relative humidity. And the factor weight value of community economic indicators is the smallest. Meanwhile, we classified the obtained evaluation factors into four classes: highly suitable area (S1), moderately suitable area (S2), general suitable area (S3), and unsuitable land area (N).

4.4. Suitability Evaluation Results

We used GIS-related software to spatially interpolate and reclassify the index factors. After applying the most accurate random forest to determine the weights of each factor, we then carried out the spatial analysis module via ArcGIS software to superimpose different factors according to the corresponding weights [30]. The equation is as follows:
S = ∑ W i * P i
where Pi is the evaluation factor value, Wi is the weight of each evaluation factor, i = 1, 2, 3… n. Based on international FAO standards, we used the natural breakpoint method to group the final results into four different classes of land: highly suitable, moderately suitable, generally suitable, and unsuitable.
A distribution map of the tea suitability evaluation was obtained in this study by calculation, as shown in Figure 6. The highly suitable area, which is also the area with the most favorable natural geographical conditions for the growth and development of tea crops, accounted for 14.13% of the total evaluated area. It was distributed in the southern and a small northern part of the Houkeng Village and Qiaoshan Village, the northern part of the ** Houkui tea, which has the largest tea industry base in the ** monkey tea as the research object and selected twelve influencing factors from the natural environment and anthropogenic factors. We then compared seven machine learning methods and selected the method with the highest accuracy for quantitative evaluation of suitability. This study can also provide scientific reference for land allocation decisions for various crops in the town and village scales, such as coffee, rice, and wheat. The evaluation results can be summarized as follows:
(1)
By comparing the prediction accuracy of the seven machine learning methods, the final results showed that RF had the highest accuracy, with a predicted AUC value of 0.86. This indicates that the machine learning methods have certain advantages in the suitability evaluation model, with high accuracy and good evaluation results, which can improve the objectivity of the model.
(2)
** monkey” tea tree growth suitable area from north to south exhibited a gradually declining trend. Highly suitable areas (14.13%) and medium suitable areas (27.25%) were mainly concentrated in the north, west, and central, specifically distributed in Houkeng, ** Houkui tea. Gehu Village tea plantations were not suitable for large-scale expansion because the yield and quality of tea planted in the area were not as good as in other areas.
Compared with the traditional weighting method, this study used the machine learning method, which weakens the bias caused by the subjective will to a certain extent; thus, it analyzes the influence of multiple factors on tea growth more objectively and scientifically. We will further improve the evaluation factors and methods, such as anthropogenic activities, tea tree pests, and diseases that affect tea tree growth and quality, in future studies. Meanwhile, improving the generalizability of the model is key to future research on land suitability evaluation of various crops. The evaluation methods can be further extended, such as use of deep learning, to combine subjective and objective evaluations from both qualitative and quantitative perspectives to inform scientific planning of crop planting.

Author Contributions

W.X. and C.Z. contributed equally to this article. Writing—original draft, Visualization, Writing—review & editing, W.X.; Methodology and Software, C.Z.; Data curation: X.C., W.W., J.H. and Y.T.; Funding acquisition, Supervision and Conceptualization, J.L.; Funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The National Key R & D Program of China (No. 2018YFD1100104), the Natural Science Foundation of Anhui Province (No. 2108085MD29), the National Natural Science Foundation of China (No. 41571400), the Offline Excellent Course of Anhui Province (No. 2021xxkc038).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used to support the findings of this study area are available from the corresponding author upon request via email.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. **nming Township geographic location map.
Figure 1. **nming Township geographic location map.
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Figure 2. Houkeng Village landscape.
Figure 2. Houkeng Village landscape.
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Figure 3. Technical roadmap of tea cultivation suitability evaluation in **nming Township.
Figure 3. Technical roadmap of tea cultivation suitability evaluation in **nming Township.
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Figure 4. Reclassification of criteria: (a) Slope, (b) Aspect, (c) Elevation, (d) Organic matter, (e) Soil pH, (f) Nitrogen, (g) Phosphorus, (h) Potassium, (i) Average temperature, (j) Relative humidity, (k) Distance from roads, and (l) Distance from rivers.
Figure 4. Reclassification of criteria: (a) Slope, (b) Aspect, (c) Elevation, (d) Organic matter, (e) Soil pH, (f) Nitrogen, (g) Phosphorus, (h) Potassium, (i) Average temperature, (j) Relative humidity, (k) Distance from roads, and (l) Distance from rivers.
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Figure 5. ROC curve comparison.
Figure 5. ROC curve comparison.
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Figure 6. Tea growth suitability distribution.
Figure 6. Tea growth suitability distribution.
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Figure 7. Distribution diagram of suitability evaluation results of existing tea gardens, where S1, S2, S3, and N represent highly suitable area, moderately suitable area, generally suitable area, and unsuitable land area, respectively. Variables are explained in Section 4.3.
Figure 7. Distribution diagram of suitability evaluation results of existing tea gardens, where S1, S2, S3, and N represent highly suitable area, moderately suitable area, generally suitable area, and unsuitable land area, respectively. Variables are explained in Section 4.3.
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Figure 8. Spatial distribution map of tea garden.
Figure 8. Spatial distribution map of tea garden.
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Table
Table 1. Factors affecting tea growth.
Table 1. Factors affecting tea growth.
CriteriaEvaluation FactorEffects
TopographicalSlopeGenerally high mountain tea planted at an altitude of approximately 400–700 m. “Tai** Houkui” is located in the **nming Township. Part of the tea plantation is located at an altitude of 700 m above sea level. As the altitude rises, the temperature gradually decreases; when the temperature falls below 0 °C, frost damage affects both the yield and quality of tea.
Aspect
Elevation
SoilOrganic matterTea trees do well in acidic soil, the most suitable pH being 4.5~5.5. When the soil pH exceeds 6.5, tree growth is inhibited. Although tea trees tolerate relatively low pH, decreasing pH (increasing acidity) has been shown to inhibit the growth of tea trees as well as affect the quality of tea leaves to a certain extent. The organic matter of high-quality tea growing terrain is generally greater than 2%, and the nutrient content of the soil will directly affect the quality of tea leaves. There is also a correlation between the nitrogen, phosphorus, and potassium content of the soil and the quality of tea leaves.
pH
Nitrogen
Phosphorus
Potassium
ClimaticAverage temperatureTea trees grow best under consistent and relatively high temperature and humidity. Generally, temperatures above 15 °C and humidity greater than 50% are appropriate.
Relative humidity
Community economic indicatorsDistance from
roads		The distance from the road will affect the efficiency of tea picking. In addition, the construction of the road will destroy the original layer of soil, which will cause a decrease in soil fertility. The distance from the water source has an effect on soil moisture and the ease of irrigation.
Distance from
rivers
Table
Table 2. The relevant acronyms in this study.
Table 2. The relevant acronyms in this study.
Nomenclature
LRlogistic regressionVvariable importance measures
RFrandom forestS1highly suitable area
XGBoostextreme gradient boostingS2moderately suitable area
AdaBoostAdaptive boostingS3general suitable area
GNBGaussian Naïve BayesNunsuitable land area
GBDTgradient boosting decision treeScomprehensive suitability evaluation value
MLPmulti-layer perceptronPievaluation factor
GIGini indexWievaluation factor weighting
Table
Table 3. Weight and analytic hierarchy results of tea suitability evaluation factors in **nming Township.
Table 3. Weight and analytic hierarchy results of tea suitability evaluation factors in **nming Township.
CriteriaEvaluation FactorWeightSuitability Class
S1S2S3N
TopographicalSlope (°)0.0945–250–5>25-
Aspect (°)0.100112.5–247.567.5–112.5
and 247.5–292.5		292.5–67.5
and −1–0		-
Elevation (m)0.100500–700300–500 and >7000–300-
SoilOrganic matter (%)0.0254–77–163–4-
pH0.1324.5–5.55.5–6.5>6.5 and 4.0–4.5-
Nitrogen (g/kg)0.1202.2–2.8>2.80–2.2-
Phosphorus (mg/kg)0.030>105–100–5-
Potassium (mg/kg)0.121>500300–500<300-
ClimaticAverage temperature (°C)0.12417–20>20<17-
Relative humidity (%)0.12270–7550–70>75-
Community economic indicatorsDistance from
Roads (km)		0.0210–0.50.5–0.80.8–1.2>1.2
Distance from
Rivers (km)		0.0110.2–0.50–0.20.5–1.0>1.0
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**ng, W.; Zhou, C.; Li, J.; Wang, W.; He, J.; Tu, Y.; Cao, X.; Zhang, Y. Suitability Evaluation of Tea Cultivation Using Machine Learning Technique at Town and Village Scales. Agronomy 2022, 12, 2010. https://doi.org/10.3390/agronomy12092010

AMA Style

**ng W, Zhou C, Li J, Wang W, He J, Tu Y, Cao X, Zhang Y. Suitability Evaluation of Tea Cultivation Using Machine Learning Technique at Town and Village Scales. Agronomy. 2022; 12(9):2010. https://doi.org/10.3390/agronomy12092010

Chicago/Turabian Style

**ng, Wenwen, Cheng Zhou, Junli Li, Weiyin Wang, **gchi He, Youjun Tu, **u Cao, and Yunhua Zhang. 2022. "Suitability Evaluation of Tea Cultivation Using Machine Learning Technique at Town and Village Scales" Agronomy 12, no. 9: 2010. https://doi.org/10.3390/agronomy12092010

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