AI-Automated System for Ingredient Planner using Machine Learning Algorithms

There are numerous delicious Indian recipes available with the same ingredients. The traditional recipes differ based on the availability of vegetables, spices, herbs, and fruits. This necessitates the need to verify the ingredients used in preparing a food recipe. This proposed system, AI-based Industrial Automation enabled with mobile access is planned and designed with the help of dataset to be the best possible solution. The aim is to develop an AI-Automated System using a Machine Learning (ML) technique called Adaptive (AdaBoost) and Gradient Boosting algorithm (AGBA) in the food sector to examine the food recipe ingredients. The data gathered for the food items with their requirements has been considered as the data set in this study. By using the data set, Industrial Automation System with Ingredient Planner app is developed and automated. The main principle behind these boosting techniques is to build a model with a training dataset and then rectify the faults present in the previously built model. This principle is sustained as long as the faults are reduced and the training dataset is estimated correctly. Further, a multi-functional approach will be implemented to employ Big Data for examining the ingredients of food menu, computational time, labor-intensive estimation time, and food wastage.