Performance Analysis on Machine Learning Algorithms with Deep Learning Model for Crop Yield Prediction

Abstract

Crop yield prediction is the task of estimating the yield of the crop in terms of kilogram per hectare by considering various features like weather conditions, soil properties, water level, location, previous year yield, etc. A Multi-Layer Perceptron neural network model and Random forest regression models are trained using the data collected for 4 major crops grown in the Karnataka region. Weather data and past yield data of 30 districts of Karnataka are collected. Weather data includes minimum, maximum and average values of temperature, humidity and pressure. These two datasets are then merged, pre-processed for training the model. To evaluate the trained models. evaluation metrics used were mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE). The results show that Multi-Layer Perceptron network and Random forest regression obtained the Mean absolute error of 12.3% and 12.4%, mean square error of 3.4% and 2.9%, root mean square error of 18.55% and 17.12% respectively. For real-time prediction, a basic web-application is built using a python web framework, Flask and the trained model is called to predict the yield.