Indian Journal of Science and Technology

Abstract

Objectives: To develop Crop Recommender System, using Decision Tree, suggesting suitable temporary crops in Laguna, Philippines based on forecasted agrometeorological variables with Time Series analysis and Autoregressive Integrated Moving Average models. Methods/Statistical Analysis: This study develops a tool to assess the suitability of crops based on forecasted variables with higher accuracy using Decision Tree and make the crop more adaptative to a changing weather patterns. Findings: Simulation process was done to evaluate and test the accuracy of the system. In forecasting agrometeorological variables: Maximum temperature, minimum temperature, relative humidity, sunshine duration, accumulated rainfall, the system garnered accuracy of 98.23%, 97.71%, 97.03%, 81.96% and 67.51% to each variable respectively. Overall, the accuracy of the agrometeorological forecasts averaged to 91.64%. For the measurement of accuracy of the system in assessing the suitable crops, the model was tested on each crop and resulted to 86.33% overall classification accuracy. In this study, it is determined that high variability of time series affects the forecast accuracy of rainfall, which model was less accurate compare to other agrometeorological variable models. Application/Improvements: TANIM, a new web-based decision support system enables decision makers to know the future condition of agrometeorological variables and track which temporary crops are suitable based from the forecast.

Keywords: Agrometeorological Variables Forecasting, Autoregressive Moving Average, Decision Tree Algorithm, Suitability Assessment, Temporary Crops