Modelling the impact of agrometeorological variables on regional tea yield variability in South Indian tea-growing regions: 1981-2015

Raj, Esack Edwin; Ramesh, K. V.; Rajkumar, R.H.

doi:10.1080/23311932.2019.1581457

Cited by 10 publications

(7 citation statements)

References 51 publications

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“…In the previous study, the prediction of tea yield was developed depending on the relationship between NDVI and leaf area index, or the relationship between NDVI and meteorological factor [24][25][26][27][28]. They were developed as multiple linear models to predict tea yield using climatic variables.…”

Section: The Forecast Tea Yieldmentioning

confidence: 99%

“…They were developed as multiple linear models to predict tea yield using climatic variables. There was a strong causal relationship between climate variables and tea yield [24,25,51]. Crown density of tea was computed based on NDVI and tea yield was computed based on crown density [23].…”

Section: The Forecast Tea Yieldmentioning

confidence: 99%

“…NDVI plays an important role in identification, assessment, and tea yield prediction [7][8][9][10][11]. The climatic variables were positively correlated with the tea yield [6,[24][25][26]. Among the climate factors, temperature variability was determined as a stronger positive effect than precipitation in the predicted tea yield [43,51,54].…”

Section: Limitation and Future Perspectivesmentioning

confidence: 99%

“…Empirical relations between the NDVI and crown density were investigated, whereas the relation between the crown density and tea yield remains to be investigated by F Fauziana [23]. In order to predict tea yield, the statistical relationship was established between climate variables, which were regarded as predictor parameters and tea yield (predictand), by [24][25][26][27][28]. All studies used meteorological data, or satellite image data, or the incorporation of these two to establish empirical models for tea yield prediction.…”

Section: Introductionmentioning

confidence: 99%

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Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Phan

Chen

et al. 2020

Remote Sensing

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Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

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Section: The Forecast Tea Yieldmentioning

confidence: 99%

Section: The Forecast Tea Yieldmentioning

confidence: 99%

Section: Limitation and Future Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Phan

Chen

et al. 2020

Remote Sensing

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“…The crop model forecasted yield by using the physiological characteristics of plants based on extensive input data to simulate crop growth and yield [25]. Machine learning-based models used historical data and do not directly rely on known physiological mechanisms for individual crops [26,27]. Multiple vegetation indices from MODIS data were applied to estimate the crop yield for wheat, corn, soybean, maize almond and tea through many other methods.…”

Section: Introductionmentioning

confidence: 99%

NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam

Phan

Chen

et al. 2021

Atmosphere

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Tea is one of the most significant cash crops and plays an important role in economic development and poverty reduction. On the other hand, tea is an optimal choice in the extreme weather conditions of Tanuyen Laichau, Vietnam. In our study, the NDVI variation of tea in the growing season from 2009 to 2018 was showed by calculating NDVI trend and the Mann-Kendall analysis to assess trends in the time series. Support Vector Machine (SVM) and Random Forest (RF) model were used for predicting tea yield. The NDVI of tea showed an increasing trend with a slope from −0.001–0.001 (88.9% of the total area), a slope from 0.001–0.002 (11.1% of the total area) and a growing rate of 0.00075/year. The response of tea NDVI to almost climatic factor in a one-month time lag is higher than the current month. The tea yield was estimated with higher accuracy in the RF model. Among the input variables, we detected that the role of Tmean and NDVI is stronger than other variables when squared with each of the independent variables into input data.

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Optimizing LSTM and Bi-LSTM models for crop yield prediction and comparison of their performance with traditional machine learning techniques

Kiran Kumar,

Ramesh,

Rakesh

2023

Appl Intell

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Modelling the impact of agrometeorological variables on regional tea yield variability in South Indian tea-growing regions: 1981-2015

Cited by 10 publications

References 51 publications

Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam

Optimizing LSTM and Bi-LSTM models for crop yield prediction and comparison of their performance with traditional machine learning techniques

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