Data Analytics in Farming: Rice price prediction in Andhra Pradesh
Pundru Chandra Shaker Reddy, G. Suryanarayana, LNC Prakash K, Sucharitha Yadala
Abstract
The variances regarding prices of horticultural commodities adversely affect the nations GDP. The ranchers are genuinely and monetarily influenced as their long stretches of difficult work go to no end. Forecast of the prices might help the farming production network in settling on vital choices in limiting and dealing with the danger in value variances. By the way of outcome of the diminution of crop yield making due to uneven atmosphere circumstances and global warming. Rice is a significant yield developed in India, with Andhra Pradesh (AP) state being second as far as creation. At present ranchers in AP are moving from rice development to different harvests on account of worth variances and environmental variation. In this research, the costs of rice in AP remain anticipated utilizing time-series using data analytics like Machine Learning (ML) models. The prototypes SARIMA, Holt-Winter's Periodic technique, and Long Short-Term Memory (LSTM) Neural Networks (NN)be situated and utilized, and their recital was assessed dependent on the RMSE esteem on the rice database whose costs from 2001 to 2020. The conclusions demonstrate that the recommended process, LSTM neural network method is actual expectant and finest method which is suitable for the data.