Multiple Crop Pest Classification Using Big Data Analytics

Abstract

Agriculture is a mixture of so many crops, like food and non-food. Food staples being rice and wheat. Indian farmers also grow pulses, potatoes, Sugar Cane. And non-food items are cotton, tea, coffee, rubber and jute. In Andhra Pradesh state Agriculture is an age old economic activity. Where many major crops are cultivated in this state, the largest problem faces in agriculture is pests attack. Pest attack not only causes loss for a crop but also affects the farmers to live a pathetic life. To classify the crops and pests a non-persistent based method like big data analytics is reliable and to analyse the data by using machine learning algorithms, which can give the efficient results. In this context, six popular machine learning algorithms like Decision Tree (DT), Random Forest Classifier (RFC), K-Nearest Neighbour (K-NN), Gaussian Naïve Bays (GNB), Support Vector Machine (SVM) and Ada Boost Classifier (ABC) are applied. Among them Random Forest Classifier(RFC) performed in time and given accurate values while compared with other methods. The Random Forest Classifier makes the agriculture sector to classify and comprehend the pests easily. The ML based pest classification system is helpful to the department of agriculture to analyse the pests in various crops and in various seasons.