Phenotype Based Smart Mobile Application for Crop Yield Prediction and Forecasting Using Machine Learning and Time Series Models

Abstract

Prediction and forecasting of crop yield recently plays a vital role in the field of Agriculture. Drastic changes in climatic conditions, changes in rainfall season, and lack of nutrients content in the soil etc., due to major factors such as rapid industrialisation, global warming and pollution. This leads to the farmers' predictions based on their own agricultural experiences on various crop yields based on external factors gone wrong. This results in farmers not getting adequate yield and suffering from financial loss. Machine learning and time series models are involved in this research work to carry out prediction and forecast of corn and soybean crop production over time through mobile application and it consist of various regression algorithms of machine learning such as multiple linear regression (MLR), decision tree regression (DTR), random forest tree regression (RFTR), k-nearest neighbour (KNN) and gradient boosting regression (GBR) are used for crop yield prediction. Time series models such as auto regression (AR), moving average (MA), auto regression integrated moving average (ARIMA) and vector auto regression (VAR) used for forecast of crop production. Comparative analysis also made between machine learning and time series models, in which GBR of machine learning outperformed other machine learning models with 92.648% predicted yield accuracy and VAR of time series model outperformed other time series models with 94.367% forecasted yield accuracy. Regression metrics such as mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) are also involved in predicting crop yields.