A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters

• Published in: Journal of ambient intelligence and humanized computing Vol. 12; no. 11; pp. 10009 - 10022
• Main Authors: Elavarasan, Dhivya, Vincent, P. M. Durai Raj
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021; Springer Nature B.V
• Subjects: Accuracy; Agribusiness; Agricultural production; Agriculture; Algorithms; Artificial Intelligence; Artificial neural networks; Computational Intelligence; Crop yield; Datasets; Decision making; Decision trees; Engineering; Error analysis; Error reduction; Food security; Forecasting; Machine learning; Mathematical models; Original Research; Parameters; Public domain; Robotics and Automation; Soil water; Splitting; Support vector machines; User Interfaces and Human Computer Interaction; Crop yield prediction; Soil parameters; Regression model; Reinforcement learning; Groundwater properties; Random forest; Climatic parameters

The development in technology and science has contributed to a vast volume of data from various agrarian fields to be aggregated in the public domain. Predicting the crop yield based on climate, soil and water parameters has been a potential re- search subject. Therefore an objective arises in integ...