A Python Framework for Pest Management and Crop Yield prediction

Abstract

In recent years, agriculture has become increasingly important as the world's population continues to grow. The demand for food grains is constantly on the rise; this exerts more pressure on farmers to maximize their crop yields. One of the biggest challenges the farmers face is managing pests and maintaining soil fertility. Farmers apply pesticides only after witnessing the pest on the crop. By this time the pest has already made an impact on the crop leading to crop loss. Traditional methods of pest management and preserving soil fertility are time-consuming and less effective.In this paper, a Python framework has been developed that can help farmers maximize their crop yield by predicting the attack of the pest on the crop and the most suitable period for spraying the pesticide.Timely and precise prediction of outbreak of pest can help in controlling the pest and improving crop yield. DM is used to forecast pest attacks. Time series data on the pest trap values is collected from the website. Here the pest considered is American Ball worm adult and the crop considered is cotton. The weekly data is collected from 1991 to 2000.On this data after preprocessing it is subjected to DM model. For prediction 10 years data is considered. ARIMA model is developed and employed on this time series data.The accuracy of the forecasting model is verified by using RMSE value. We will go through the benefits of using this framework and describe how it can make farming more efficient and productive.