Weighted association rule mining for the occurrence of the insect pest Helicoverpa armigera(Hübner) related with abiotic factors on cotton
M. Pratheepa, Abraham Verghese, H. Bheemanna · International Conference on Computing for Sustainable Global Development · 2016
Helicoverpaarmigera (Hubner) is a polyphagous insect pest which feeds on 181 plant species and damages important agricultural crops in turn leads to huge loss in production. There is a gap on understanding the role of abiotic factors like maximum temperature, minimum temperature, relative humidity and rainfall on this pest incidence. The data mining technique weighted association rule mining has been proposed for finding the optimized IF-THEN rules for the pest occurrence. It has been found that the pest incidence will be high when maximum temperature ranges from 28.3–33.1°C, minimum temperature ranges from 16.5–22.07°C and relative humidity ranges from 45.5–60%. Forewarning the farmers based on this method helps to take up the pest control measures in time so that crop loss can be minimized.