Classification based Interactive Model for Crop Yield Prediction: Punjab State

Machine Learning (ML) is a vital decision support mechanism in predicting crop yield, by comprising information like what to cultivate and how to improve and maintain the quality of soil, humidity etc. Numerous ML techniques have been practiced in this field to aid crop yield forecasting research. Forecasting crop yield much before cultivation, would help the farmers take commendable measures for marketing and storage. This paper aims to propose an improved classification based interactive model for crop yield prediction for the state of Punjab in India, before cultivating onto the agricultural field based upon major parameters viz. climate and production area. Also, the performance of the system is analyzed using four ML algorithms: “Random Forest”, “Lasso-Regression”, “Linear-Regression”, and “Support Vector Machine”. Lasso-Regression has not been used for predicting crop yield yet. The achieved 96.72% accuracy of the system ascertained its effectiveness in helping farmers to select most appropriate crops for harvesting.