A multiple linear regressions model for crop prediction with adam optimizer and neural network Mlraonn

Abstract

Due to the increase in population, demand for the food is increasing day by day. Crop prediction is necessary or need of the hour to fill the gap between the demand and the supply. Instead of following a traditional system for crop selection method, a successful crop selection for the given soil properties will help the farmers to get the expected crop yield. The objective of the proposed work is to develop one such system. The proposed system is developed using real data with various soil parameters acquired from soil laboratory located in Chennai. This system uses 16 parameters of soil which includes all the micro, macro nutrients along with that pH, EC, OM values and the recommended crop for the soil parameter. The proposed Mlraonn (Multiple Linear Regression with Adam Optimization in Neural Network) model is developed using Keras software mainly used for Deep Learning. A neural network approach is used to construct a regression model. The model is evaluated with Loss Metrics such as RMSE, MSE, and MAE. The proposed algorithm is compared with the existing standardized machine learning algorithms. It is found that the proposed algorithm gave very minimal error as output in all the above three categories of loss metrics than the standardized algorithm such as Random Forest Regression and Multiple Linear Regression.