ENHANCED WEED AND CROP SPECIES CLASSIFICATION UNDER GREENHOUSE CONDITIONS THROUGH OPTIMIZATION OF MACHINE LEARNING MODELS AND ENSEMBLE STRATEGIES

1. Introduction 1.1 Background and Motivation 1.2 Objectives of the Study 1.3 Significance of the Research 1.4 Structure of the Thesis 2. Literature Review 2.1 Overview of Crop and Weed Classification 2.2 Machine Learning in Agricultural Sciences 2.3 Ensemble Methods in Classification 2.4 Existing Challenges in Greenhouse Conditions 3. Methodology 3.1 Research Design and Approach 3.2 Data Collection and Preparation 3.3 Machine Learning Models Selection 3.4 Ensemble Strategy Development 4. Data Analysis 4.1 Exploratory Data Analysis 4.2 Feature Selection and Importance 4.3 Model Training and Validation 4.4 Comparison of Model Performance 5. Optimization of Models 5.1 Hyperparameter Tuning Techniques 5.2 Regularization and Overfitting Handling 5.3 Cross-Validation Strategies 5.4 Evaluation of Optimized Models 6. Implementation of Ensemble Strategies 6.1 Bagging and Boosting Methods 6.2 Stacking and Blending Approaches 6.3 Performance Metrics for Ensembles 6.4 Case Study under Greenhouse Conditions 7. Results and Discussion 7.1 Key Findings and Interpretations 7.2 Comparison with Existing Studies 7.3 Implications for Agricultural Practices 7.4 Limitations of the Research 8. Conclusion and Future Work 8.1 Summary of Contributions 8.2 Recommendations for Practitioners 8.3 Directions for Future Research 8.4 Final Thoughts on Study Impact