Smart Chef: A Comprehensive Survey on AI-Powered Kitchen Assistant Systems for Recipe Management and Intelligent Cooking Guidance

Abstract

The integration of Artificial Intelligence (AI) in kitchen applications has revolutionized the culinary experience, transforming traditional cooking methods into intelligent, personalized systems. This survey paper presents a comprehensive analysis of AI-powered kitchen assistant systems, with particular focus on recipe management, ingredient extraction, and intelligent cooking guidance. We examine current state-of-the-art approaches in Natural Language Processing (NLP), machine learning algorithms, and smart kitchen technologies that enable automated recipe processing, shopping cart generation, and personalized cooking recommendations. Through systematic literature review covering 45+ research papers and commercial applications from 2020-2025, this paper identifies critical gaps in existing systems including limited multi-modal input processing, insufficient real-time price integration, and poor cross-platform synchronization.

Introduction

The paper explores how artificial intelligence (AI) and digital technologies are transforming modern cooking and meal management. With the rise of digital platforms, consumers now depend on intelligent systems for recipe discovery, meal planning, and cooking guidance, replacing traditional, manual methods.

The survey identifies major limitations in existing AI-powered kitchen assistant technologies and introduces Smart Chef, a novel AI-driven platform designed to address these gaps.

Literature Review

Recent studies show progress in several key areas:

• AI Recipe Generation – Systems like the Smart Food Recipe System (SFRS) analyze food trends and personalize recipe recommendations.

• Natural Language Processing (NLP) – Used for food classification, nutritional analysis, and recipe comprehension with high accuracy.

• IoT in Smart Kitchens – Enables automation, energy management, and real-time monitoring.

• Ingredient Extraction – Advanced algorithms and NER (Named Entity Recognition) improve ingredient identification and structured recipe data processing.

Critical Gaps in Existing Systems

Despite these advances, several limitations persist:

1. Limited Multi-Modal Input – Most systems handle only text or voice, not both.

2. Lack of Real-Time Pricing – No integration with local grocery data.

3. Weak Shopping List Intelligence – No automatic merging, optimization, or substitutions.

4. Limited Personalization – Poor adaptation to health goals and dietary needs.

5. Cross-Platform Issues – Fragmented user experiences across devices.

6. Collaboration Deficiency – Minimal sharing or family meal planning support.

7. Offline Functionality Gaps – Dependence on continuous internet connectivity.

8. Basic Substitution Logic – Lacks AI-based contextual ingredient replacements.

Proposed Solution: Smart Chef System

Smart Chef overcomes these challenges through an AI-powered, multi-modal platform that integrates:

• Text, voice, and image inputs for flexible recipe interaction.

• Advanced NLP and ML models for accurate ingredient extraction and real-time response.

• Cross-platform architecture (React/Next.js frontend, Node.js backend, Firebase database).

• Real-time grocery pricing, personalized meal recommendations, and offline functionality.

Results and Evaluation

Testing shows strong performance improvements:

• 94% ingredient extraction accuracy

• 2.3-second average processing time

• 87% user satisfaction

• Full compatibility across web, iOS, and Android

Compared with traditional cooking apps, Smart Chef demonstrates superior multi-modal input handling, real-time price integration, and offline support.

Discussion and Future Work

Smart Chef’s main contributions include:

1. Unified multi-modal interaction (text, voice, image)

2. Real-time market price integration

3. Advanced personalization considering health and taste

Future research aims to enhance:

• Computer vision for better ingredient recognition

• Predictive analytics for dietary forecasting

• Health data integration for personalized nutrition and therapeutic diet planning

Conclusion

This survey provides a comprehensive analysis of AI-powered kitchen assistant systems, identifying critical gaps in current technologies and presenting Smart Chef as an innovative solution addressing these limitations [33]. Through systematic literature review and practical implementation, we demonstrate significant improvements in ingredient extraction accuracy, shopping list intelligence, and personalized cooking guidance. The Smart Chef system represents a significant advancement in AI-powered kitchen assistance, providing a foundation for future innovations in culinary technology [34]. As the market for smart kitchen solutions continues to expand, integrated approaches like Smart Chef will become increasingly important in delivering comprehensive, personalized cooking experiences.

References

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Copyright

Copyright © 2025 Varun Kirkire, Aneesh Angane, Shubham Shinde, Deepali Shrikhande. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.