AI Framework for Fruit Quality Assessment: Integrating CNN-Based Spoilage Detection, Price Estimation, and LLM-Powered Insights

The AI utilizes Convolutional Neural Networks (CNNs) combined with Large Language Models (LLMs) to deliver complete fruit-quality evaluation which includes spoilage detection and price assessment. Through deep learning image analysis the system identifies different ripeness stages of fruits at high speed which results in fast spoiled produce detection. The efficiency of the system increases because of LLM functionalities that deliver important product shelf life instructions while supplying preservation methods and proper usage guidelines. The price calculation system employs data-driven rules to measure product worth based on ripeness indicators which creates balanced pricing structure for suppliers and customers. A coalition of advanced evaluation methods and AI automation systems generates optimal fruit quality testing which enhances market evaluations and retail point-of-sale assessments. The proposed method enables both precise and efficient fruit classification as well as promotes sustainable consumption through waste reduction to enhance buying choices.