Surveillance and Predictive Information System for Tea Smallholdings (SPIS-TS)

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.