Implementation of Machine learning Algorithms to Identify Freshness of Fruits

The main goal of the study is to determine which algorithm, using classic machine learning and deep learning models, accurately predicts photos when given by the user. In the first part of the study, we will see how machine learning applications and how they are used in modern life. The second half of the study will explain the various parameters that have been projected by our study and how well the designs are performing with the given set of data. Finally, deep learning techniques showed better accuracy with lower false positive rates in comparison to machine learning models. Within the food business, computerised classification of fruit freshness plays a significant role. Fruit degradation must be discovered at every stage of the process, from manufacture to consumption. Traditional procedures for identifying fruit deterioration are modest, difficult, subjective, and time consuming, necessitating the introduction of precise automatic technologies that may be used for industrial reasons. This study looks at a dataset of three distinct fruits to distinguish between fresh and rotting fruits. Conventional methods like machines and various types of deep learning are used in a typical vision-based system. The utmost realisation rates are usually achieved particularly when we use convolutional neural networks-cantered elements and deep learning models after testing the model for numerous tentative layouts, including both binary and multi-class classification tasks.