Optimization and decision-making in relation to rainfall for crop management techniques

Abstract

Disease prediction has a high degree of uncertainty which is due to the complex and imperfect nature of symptoms that are used in diagnosis. Diseases continue to be a threat to crop yield and investments even though technological advancements have been made in agricultural sector. Clinically screened database has been taken as knowledge base for weather and crop symptoms. The present work highlights the degree of effect of different weather parameters on rainfall and then further utilizes the findings for decision-making on crop production including disease detection and crop selection. Parameters used in this experiment are Wind Speed (WS), Relative Humidity (RH) and Temperature (T). Taguchi Orthogonal arrays (OA) will be used for data optimization. Parameter optimization is done with the help of ANOVA (Analysis of Variance).