Predictive Modeling of Different Treatments for Physiological and Nutritional Parameters of Ocimum Sanctum

An increasing demand for superior quality crops has led to investigating the current trends in agriculture. One of the main factors impacting this is environmental stress. Even with certain plant defense mechanisms to combat stress, not all plants can tolerate the impact induced by abiotic stress, therefore compromising the overall physiological and nutritional parameters greatly. This research focuses on the effects of stress on the Ocimum sanctum plant, its corresponding parametric variation data is further processed and analyzed by predictive models. Abiotic stress: Plants were exposed to drought, jasmonic acid, potassium nitrate, hydrogen peroxide, at varied intensities, and parameters - plant height, relative water capacity, protein, chlorophyll content, and photosynthetic rate were subsequently recorded for 50 days. KNN classification model was used to predict the treatment required for any given parameter. A comparative study between Linear and Non-Linear Regression models with a common goal of predicting one parameter from the other known parameter was done and inferred that the Non-Linear Regression model performed well and had better accuracy.