An Advanced Hybrid Xgboost–Particle Swarm Optimization-Based Framework For Intelligent Fruit Quality Prediction Through Optimal Feature Selection And Performance Enhancement

Dr. K.  Manivannan; Dr. V. Parthasarathy

doi:10.29284/wyhh6s52

Authors

Dr. K. Manivannan Assistant Professor, Department of Computer Science, Arignar Anna Government Arts College, Villupuram, Tamil Nadu, India
Dr. V. Parthasarathy Assistant Professor, Department of Computer Science, Dr. M.G.R. Government Arts and Science College for Women, Villupuram, Tamil Nadu, India

DOI:

https://doi.org/10.29284/wyhh6s52

Keywords:

Fruit Quality Prediction, XGBoost, Particle Swarm Optimization (PSO), Feature Selection, and Machine Learning.

Abstract

Fruit quality prediction is an important task in agriculture and food industries for improving product quality and reducing manual effort. Traditional methods of quality assessment are often time-consuming and may produce inconsistent results due to human involvement. To overcome these limitations, this research proposes an Advanced Hybrid XGBoost–Particle Swarm Optimization (XGBoost–PSO) Framework for fruit quality prediction. The proposed model combines XGBoost with Particle Swarm Optimization to select important features and improve prediction performance. Initially, preprocessing techniques such as data cleaning and normalization are applied to enhance data quality. PSO is then used to identify optimal features and reduce unnecessary information from the dataset. The selected features are provided to the XGBoost model for classification and prediction of fruit quality. Experimental results show that the proposed framework improves prediction accuracy and overall system performance compared with conventional machine learning methods. The proposed approach can support intelligent agricultural systems by assisting farmers and industries in efficient fruit quality assessment and decision-making.

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