REAL-TIME FRUIT RIPENESS CLASSIFICATION USING VOC PROFILING AND DECISION TREE ALGORITHMS: A SOLUTION FOR REDUCING POST-HARVEST LOSSES

Abstract

In Pakistan, the agricultural sector is vital for economic stability, particularly through fruit cultivation. However, accurately determining fruit ripeness poses a significant challenge, resulting in considerable post-harvest losses and diminished market value. Traditional ripeness assessment methods are often subjective, inconsistent, and labor-intensive. To address this issue, we developed an innovative AI-based fruit ripeness detection system utilizing MQ gas sensors. This system detects volatile organic compounds (VOCs) emitted by fruits during the ripening process, providing real-time data on gas concentrations associated with ripeness stages. The sensor data is processed using advanced artificial intelligence algorithms, specifically a decision tree model, to classify fruits as "ripe" or "unripe." By implementing this technology, farmers and vendors can significantly reduce post-harvest losses and enhance the quality of produce while improving overall supply chain efficiency. The decision tree algorithm effectively analyzes patterns in sensor data to make accurate predictions about fruit ripeness. This project represents a substantial advancement in modernizing agricultural practices in Pakistan, contributing to sustainable development and economic growth. The integration of cutting-edge sensor technology with machine learning not only addresses the critical challenges of fruit ripeness detection but also paves the way for innovative solutions in the agricultural sector.