Leveraging Artificial Neural Networks for Fruit Quality Prediction: Advancements in Food Technology and Quality Control

For the food and agriculture industries to guarantee consumer happiness and cut waste, accurate fruit quality prediction is essential. Artificial Neural Networks (ANNs) are used to evaluate fruit eminence based on characteristics such as color, consistency, volume, and chemical content is investigated in this paper. Current models do a good job of classifying fresh fruits, but they are not very good at spotting rotten ones, which is crucial for reducing waste and preserving quality control. This study fills this gap by compiling a dataset of both fresh and rotting fruits in order to establish a comprehensive strategy. To distinguish between them, the ANN model is trained on a variety of photos. Additionally, fruit maturity is evaluated using color changes and OpenCV, a popular computer vision toolkit. Fruit quality assessment and classification are improved by this integrated method. The suggested approach helps with fruit sorting and guarantees that buyers receive high-quality product, which benefits sectors like retail and agriculture. The ultimate objective of this project is to increase sustainability and efficiency in the food supply chain by automating quality monitoring throughout the application of state-of-the-art ML algorithms.