AN EFFECTIVE APPROACH FOR TURMERIC GROWTH DETECTION USING MULTILEVEL LINEAR ALGORITHM
Turmeric is a major cultivated crop in India. Some growing factors affect the yield and quality of turmeric production. In this paper, multilevel linear regression method is proposed to predict the growth of turmeric. The proposed method multilevel linear regression is predicting the turmeric growth level from turmeric yield dataset. Turmeric growth can predict through different statistical methods such as Linear Regression (LR), Polynomial Regression (PR), Decision Tree (DT) and Naïve Bayes. These algorithms are performing less due to prediction of turmeric growth based on accuracy and time. These algorithms give huge difference in prediction such as accuracy level and speed. To solve the above problem, turmeric yield data fed to the pre-trained for prediction of turmeric growth. The proposed method multilevel linear regression gives high accuracy prediction compared to other statistical algorithms. Multilevel linear regression method gives high accuracy of about 94% compared to conventional methods.
Turmeric yield data, Polynomial Regression (PR), Multiple Linear Regression (MLR)