SPIN: A web-based application for exploring stored-product insects

Abstract

Due to the problems of pests in post-harvest storage, the storage owner usually copes with this problem by using pesticides. However, those pests can develop resistance against chemicals in pesticides. As the pests improve resistant level to pesticide, the storage owner has to use higher dose of chemicals. This might raise the environmental and consumer safety problems. With these issues, we propose a web-based application, namely SPIN to collect location-based resistance data of stored-product insects and other factors (e.g., temperature, relative humidity, etc.) which might effect to the spread of the insects. Also, we use a machine learning algorithm, namely, decision tree for classification, to generate a prediction model for the spread of the insects based on the historical data. With SPIN, the outputs can be used in making plans to decrease the use of pesticide. This could also encourage the search for alternative pest control approaches which are more environmental friendly and much safer for consumers.