Crowdsourced Predictive Analysis for the International Trade of South Indian Coir Products in Ayurveda, Textile and Agriculture

Abstract

Business intelligence and predictive analysis has emerged as an important area in research. It replicates the impact of problems related to data, to be solved in business organizations. Indian coir industry is one among the small scale cottage industries and it contributes a great amount to the economy of South India. To grab the increasing challenges of agricultural industry, we have to monitor the complex agricultural systems by means of recent digital technologies. The modern digital technologies can monitor the physical environment in the agriculture sector and can analyze the data in a very slight amount of time. The analysis of these modern digital technologies would enable the industries to extract the current state of art and thus can improve the productivity. Although the predictive analysis becomes worldwide, it has not yet been applied in the agriculture sector widely. In this paper, we propose learning based novel approach for international trade analysis. The learning algorithm proposed here is back propagation algorithm with fuzzy linguistic model. Right here we gathered the dataset from coir exhibitions. It highlights the increasing opportunities of machine learning in agriculture towards an intelligent trade analysis through crowdsourcing. Here we compare the proposed approach with various decision tree algorithms such as C4.5, ID3, CART, Linear NN, Naïve Bayes and fuzzy approach and shows the effectiveness of our algorithm. It will help in decision making process primarily based on the current situation.