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
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-02752-y

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 integrating the data with agrarian processes for crop enhancement. In this paper, a new hybrid regression-based algorithm, Reinforcement Random Forest is proposed which displays significantly enhanced performance over traditional machine learning techniques like the random forest, decision tree, gradient boosting, artificial neural network and deep Q-learning. The new strategy executes reinforcement learning at every selection of a splitting attribute amid the process of tree construction for the efficient utilization of the available samples. They analyze the variable significance measure to select the most substantial variable for node splitting process in the model development and promotes efficient utilization of training data. This integrated hybrid procedure provides significant enhancement over prevailing strategies specifically for sparse model structures. Besides performing internal cross-validation, the proposed approach requires less parameter tuning, reduces over-fitting, faster calculation and more transparent. The experimented models are evaluated with various assessment metrics like Root Mean Squared Error, Mean Squared Error, Determination Coefficient and Mean Absolute Error. The results obtained delineated that the proposed approach performs better with reduced error measures and improved accuracy of 92.2%.