An AI Framework Model for Smart Prediction of Ripeness in Regional Fruits and Vegetables

It is imperative to take great measures of nutrition facts to stay away from different illnesses. The Standard admission of various nutrition facts are basic for keeping the correct equilibrium of nutrition content in the human body. In this project, a web-based application for estimating the condition of fruits/vegetables and nutrition facts of food improving individual's utilization propensities for wellness is developed. Additionally, by incorporating ripeness level estimation, the project offers a more comprehensive understanding of the life cycle and quality of fruits and vegetables, enhancing its utility and practicality. The nutrition facts portion of a fruit or vegetable is distinguished utilizing standard picture dataset utilizing various advances such as pre-processing, attribute extraction, training and categorization using shape and size with the help of machine learning techniques. Fruit or vegetable nutrition facts object dimensions are determined using image processing techniques. The final step is to estimate the nutrition facts (calories,protein,iron,calcium,potassium) in the given fruit or vegetable and provide the users, patients with optimum solutions for fruit or vegetable intake. This project is about Fruits-Vegetable classification and nutrition facts prediction. For this proposed system Convolutional Neural Network, KNN and Random Forest are employed. The CNN model achieves a higher accuracy of99.21. Hence the CNN model performs better than other models in an Integrated Approach to Fruits and Vegetables. It is a simple web application that every user can use it. User need to upload the image of any fruit or vegetable. Our system will automatically classify the Image and it will give you the prediction about the name of fruit or vegetable, and now it includes one another module which will give the nutrition facts of the predicted image.