"Enhancing Precision in Tulsi Leaf Infection Classification: A Stacking Classifier Ensemble Strategy"
Keywords:
Stacking Classifier, Precision Agriculture, Machine Learning, Image ClassificationAbstract
Tulsi, often known as Holy Basil (Ocimum sanctum), is a herb with cultural significance and health benefits. Researchers and farmers have been concerned about the prevalence of illnesses that harm tulsi leaves in recent years. In order to determine the best model for early identification and intervention, we do a thorough evaluation of many stacking classifier algorithms for the prediction of tulsi leaf diseases in this work. The collection of tulsi leaf imagery in the dataset is broad and includes labels designating various disease conditions. We investigate the efficacy of stacking classifiers by employing a blend of foundational models, such as Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and Multi-layer Perceptron (MLP) Classifier. Every base model offers a different perspective on the traits connected to both healthy and infected tulsi leaves. We compare various stacking classifier setups based on their accuracy, precision, recall, and F1 score. We take into account differences in the makeup of base models and how model performance is affected by hyperparameter adjustment. Furthermore, we use cross-validation methods to evaluate the five models' generalizability. Farmers and researchers can rely on the model B that is found to have the best predictive performance with an average accuracy of 98.25%, since it provides a strong means of early disease diagnosis and management. This study advances precise farming methods, encourages tulsi cultivation that is sustainable, and guarantees the plant's continued use in traditional medicine.
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