Learning dynamics of pesticide abuse through data mining

Abstract

Recent studies by agriculture researchers in Pakistan have shown that attempts of crop yield maximization through pro-pesticide state policies have led to a dangerously high pesticide usage. These studies have reported a negative correlation between pesticide usage and crop yield in Pakistan. Hence excessive use (or abuse) of pesticides is harming the farmers with adverse financial, environmental and social impacts. In this work we have shown that how data mining integrated agricultural data including pest scouting, pesticide usage and meteorological recordings is useful for optimization (and reduction) of pesticide usage. The data used in this work has never been utilized in this manner ever before. We have performed unsupervised clustering of this data through Recursive Noise Removal (RNR) heuristic of Abdullah and Brobst (2003). These clusters reveal interesting patterns of farmer practices along with pesticide usage dynamics and hence help identify the reasons for this pesticide abuse.