FOODWASTE AI: PERSONALIZED HOUSEHOLD FOOD WASTE PREDICTION AND SMART RECOMMENDATION SYSTEM

Author

Adula Supriya, Banam Varshini Priyamvada


Abstract

 One of the most significant causes of waste of food at the household level is excessive purchasing of foods, inappropriate management of inventory, and inability to consume the purchased foods before the expiry date. All these activities result in wastage of both food and money. In the current research, there will be suggested a solution to the problem in question, which will be FoodWaste AI software developed for home usage. With the help of the system, users will be able to monitor their food inventory, monitor the expiry date of their foods, and learn about which foods have more risks of turning into wastage products. Within the system, there will be created a machine learning model using the Random Forest algorithm to make predictions about food wastage probability using food and inventory characteristics. Additionally, there will be provided suggestions on recipes of meals and festival planning with estimation of the necessary grocery quantity. The technology to use is React.js frontend, Node.js and Express.js backend, MongoDB Atlas database, and Python machine learning library.



Keywords

Food Waste Management, Machine Learning, Random Forest, Food Inventory, Expiry Monitoring, Waste Prediction, Recipe Recommendation, Household Food Management, Festival Planning



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References


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