Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
The purpose
of this study is to solve the issues that tea smallholders experience in Sri
Lanka's critical tea sector by introducing a complete Surveillance and
Predictive Information System. Given that smallholder farmers are responsible
for more than 75 percent of tea output while managing just 60 percent of tea
land, it is clear that technologically driven solutions are required. In order
to improve both productivity and income, the system integrates features such as
disease diagnostics, cost prediction, yield optimization, and market
forecasting. The system provides smallholders with actionable insights by
utilizing cutting-edge techniques such as Convolutional Neural Networks (CNNs)
for disease identification, Support Vector Machines (SVMs) for disease prevention,
Autoregressive Integrated Moving Average (ARIMA) for cost prediction, Linear
Regression for yield optimization, and Long Short-Term Memory (LSTM) for market
forecasting. This method provides instruments for disease control, cost
estimation, improved yield, and educated decision-making, all of which
contribute to the expansion and continued viability of the tea business.
Country : Sri Lanka
IRJIET, Volume 7, Issue 10, October 2023 pp. 115-122