Regression Analysis to Estimate Morphology Parameters of Pepper Plant in a Controlled Greenhouse System

Abstract

Regression analysis to predict growth indices of plant is essential for understanding the relationship between the total leaf area, production of fresh weight and dry matter, and expansion of the plant growth. An experiment was conducted to develop regression models for estimating leaf area, fresh weight, and dry weight from measurements of plant height at the vegetative phase of hot pepper (Capsicum annuum Linnaeus) grown in biodegradable pots in a greenhouse. Five models were evaluated and compared: linear regression model, two-order polynomial regression model (P. order 2), three-order polynomial regression model (P. order 3), four-order polynomial regression model (P. order 4), and power regression model. The models were compared using the coefficient of determination (R2), Pearson’s correlation coefficient (r), root mean square error (RMSE), relative standard error (RSE), and mean absolute percentage error (MAPE). Power regression involving plant height demonstrated the highest R-square among the other models with minimum error estimate for the expected leaf area (R2 > 0.96, r > 0.98, RMSE 0.98, r > 0.99, RMSE 0.97, r > 0.98, RMSE < 0.03, RSE < 0.02, and MAPE < 11.7) of the plant considering both the fit and degree of adjustment, and the interpretation of the model. This study creates scope for further experimentation on various species of crops by changing management practices under different environmental conditions to enhance knowledge and understanding of the growing patterns of plants.