This paper is published in Volume-4, Issue-4, 2018
Area
Artificial Intelligence
Author
Karthik Hosur
Org/Univ
CMR Institute of Technology, Bangalore, Karnataka, India
Pub. Date
26 July, 2018
Paper ID
V4I4-1352
Publisher
Keywords
Artificial neural network, Crop cost prediction, Feed forward back propagation, Mean squared error

Citationsacebook

IEEE
Karthik Hosur. Agricultural crop cost prediction using Artificial Neural Network, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Karthik Hosur (2018). Agricultural crop cost prediction using Artificial Neural Network. International Journal of Advance Research, Ideas and Innovations in Technology, 4(4) www.IJARIIT.com.

MLA
Karthik Hosur. "Agricultural crop cost prediction using Artificial Neural Network." International Journal of Advance Research, Ideas and Innovations in Technology 4.4 (2018). www.IJARIIT.com.

Abstract

Forecasts of agricultural production and prices are intended to be useful for farmers, governments, and agribusiness industries. Because of the special position of food production in a nation\'s security, governments have become both principal suppliers and main users of agricultural forecasts. Artificial neural networks have been demonstrated to be powerful tools for modeling and prediction, to increase their effectiveness. Crop prediction methodology is used to predict the suitable crop for a field by considering the parameters: Temperature, Rainfall, Humidity, Soil properties- ( PH, nitrogen, phosphate, potassium, organic carbon, calcium, magnesium, sulfur, manganese, copper, iron )and cyclonic patterns. One of the major concern in Crop Cost Prediction (CCP) is to manage a large database with maximum attributes. In order to avoid these difficulties, a methodology named Artificial Neural Networks (ANN) with Feed Forward Back Propagation (FFBP) scheme is employed for accurate CCP. In this paper, the cost prediction accuracy is enhanced by minimizing the Mean Squared Error (MSE) by means of forecast values. Therefore, this proposed methodology outcome shows a clear idea about crop price and the crop yield estimation.