International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
A REVIEW PAPER ON Smart Kitchen Assistant for Ingredient Detection and Recipe Guidance Using Edge Artificial Intelligence Vijay Chakole1 ,Ayush Sarode2,Raju Malekar3,Muskan Kulkarni 4,Ravisha Kolhe5 , Khushi Chakole6 1Head f Dept , Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India 2345UG student, Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------require manual input and active searching, which may not Abstract - The rapid advancement of embedded provide immediate or intuitive assistance.
computing and artificial intelligence has significantly influenced the automation of domestic environments. Among various home activities, cooking remains a routine yet decision-intensive task that often requires users to determine suitable recipes based on available ingredients. This research presents the design and implementation of a Smart Kitchen Assistant capable of detecting food ingredients using image processing techniques and providing appropriate recipe guidance through an interactive display interface. The proposed system operates entirely on edge hardware using a Raspberry Pi, a camera module, and locally stored data resources, thereby eliminating dependence on internet connectivity. A lightweight convolutional neural network model is deployed using an optimized inference engine to enable real-time ingredient classification on resource-constrained hardware. Recipes are retrieved from an offline structured database and presented via a user-friendly touch interface, while a secondary display provides system feedback. The study discusses architectural design, processing workflow, hardware-software integration, algorithm formulation, and experimental observations. Results indicate that edge-based processing achieves acceptable accuracy and response time for domestic applications while ensuring privacy and reliability. The proposed system demonstrates the feasibility of developing an affordable intelligent cooking assistant suitable for smart home environments.
Recent advancements in compact computing platforms and machine learning algorithms have enabled the development of systems capable of performing complex image recognition tasks directly on embedded hardware. Edge computing, which refers to processing data locally rather than transmitting it to remote servers, offers significant advantages including low latency, enhanced privacy, and reduced dependency on network infrastructure. These characteristics make it particularly suitable for domestic kitchen environments where reliability and responsiveness are critical. The Smart Kitchen Assistant proposed in this research is designed to bridge the gap between ingredient availability and recipe decision-making. By combining computer vision with embedded artificial intelligence, the system automatically detects ingredients placed before a camera and retrieves suitable recipes stored in a local database. The objective of this work is to design a compact, affordable, and efficient prototype capable of operating independently without cloud support, thereby demonstrating the practicality of intelligent edge-based cooking assistance.
2. SYSTEM DESIGN AND ARCHITECTURE The overall architecture of the proposed system centers on a Raspberry Pi single-board computer functioning as the primary processing unit. The Raspberry Pi is selected due to its balance between computational capability, costeffectiveness, and compatibility with peripheral devices. A web camera is connected to capture images of ingredients placed within a predefined detection region. The captured data is processed locally using an optimized inference model.
Key Words: Smart kitchen, Ingredient detection, Edge computing, Embedded vision, Raspberry Pi, Recipe recommendation, Artificial intelligence.
1. INTRODUCTION The integration of intelligent systems into everyday domestic appliances has transformed the concept of smart homes from theoretical models to practical implementations. Cooking, being an essential daily activity, presents significant opportunities for automation and intelligent assistance. Many individuals experience uncertainty when selecting meals based on limited available ingredients, leading either to repetitive cooking patterns or increased food wastage. Conventional solutions such as recipe books or mobile applications
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The system incorporates a touch-enabled display that serves as the primary user interface. This interface allows users to initiate scanning, view detection results, and read detailed recipe instructions. In addition to the main display, an auxiliary Inter-Integrated Circuit (I2C) Liquid Crystal Display provides concise system status updates such as readiness, processing state, or detection confirmation. All operational software components,
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