Fuzzy Rough Set Theory (FRST) Classifier for Pest Prediction in India

Abstract

Total cotton production is affected by an important factor called cotton pests occurrence. During the growth of cotton, environmental factors having great dependence especially during climate change. In multi-disciplinary agri-technologies domain, for data intensive science, for creating new opportunities, machine learning with big data technologies is used as a high-performance computing technique and good results are produced using this. However, running time and accuracy are highly important task. Affected crop pests are only predicted using available machine learning techniques. But there wont be any accurate level of pest affection by cotton. In this paper, proposed a Fuzzy Rough Set Theory (FRST) for handling this pest prediction level. There are four major steps in this proposed work, namely, results evaluation, prediction, pre-processing and dataset collection. From 2010-2020, from All India Coordinated Research Project (AICRP) collected the dataset in the first stage. Irrelevant features are removed by performing pre-processing in the second stage and for prediction, needed features only considered. However, for prediction, weather parameters like Rainfall (RF), Relative Humidity-Evening(RH-E), Relative Humidity-Morning(RH-M), Minimum Temperature(Min-Temp) and Maximum Temperature(Max-Temp) are used. For pest prediction, only these weather parameters are used and from original dataset removed the remaining parameters. This produces a pre-processed dataset. For pest prediction, proposed a Fuzzy Rough Set Theory (FRST) classifier in third step. Cotton pest pre-processed dataset is used for training FRST. Activation corresponds to high approximation and lower approximation functions. Weather factors belonging to higher cotton pests are predicted in this result. For future pest, FRST network can be used as a better predictor according to pest records. Moreover, better performance is shown by proposed FRST network, when compared with traditional machine learning techniques like Random Forest (RF) and Multi-Layer Perceptron (MLP). At last, these pest prediction techniques effectiveness is measured using metrics like F-measure, Area Under the Curve (AUC), and Accuracy (ACC).