Remote Sensing Applications to Infer Yield of Tea in a Part of Sri Lanka

Mukherjee, Saumitra; Balasuriya, Jayasekara; Aruna, Don; Kumara, Pradeep; Singh, Chander Kumar

doi:10.1007/978-1-4614-4633-0_4

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…N. Rama Rao et al [32] used NDVI, simple ratio and TVI for tea yield prediction based on leaf area index and weather parameters. Saumitra et al [33] applied the remote sensing and GIS as tool to infer the potential of tea product by using NDVI image. Nitin K. et al [34] attempted to build a tea yield prediction model based on LAI value derived from IRS-1C LISS-III sensor.…”

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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Section: Introductionmentioning

confidence: 99%