7 AI in food production and processing: applications and challenges
-
Vidhi Jain
Abstract
Artificial intelligence (AI) is used nowadays to assist research and development in food industryfood industry. The use of AI in the food industry ranges from food productionfood production to processing including all features of the production of food constituents, crop management, precision agriculture up to food qualityfood quality as well as safety. AI has become pivotal in strengthening food safetyfood safety, production, and marketing. This chapter highlights the applications of AI in the food sector, examining their impacts on food production, assessing quality and safety of food by smart sensors, risk management, supply chain management in the food business, and so on. AI can empower precision agricultureprecision agriculture; farmers can improve crop supervision techniques and increase productivity by getting real-time information on soil composition, moisture levels of soil, weather patterns and informed decisions about irrigation, pest management, as well as harvesting, etc. This chapter pronounces the current state of AI in the food industry, its benefits, demerits, and challenges and outlines the future trends in the food industry like augmented reality with AI and AI-driven smart packaging to improve and monitor food quality.
Abstract
Artificial intelligence (AI) is used nowadays to assist research and development in food industryfood industry. The use of AI in the food industry ranges from food productionfood production to processing including all features of the production of food constituents, crop management, precision agriculture up to food qualityfood quality as well as safety. AI has become pivotal in strengthening food safetyfood safety, production, and marketing. This chapter highlights the applications of AI in the food sector, examining their impacts on food production, assessing quality and safety of food by smart sensors, risk management, supply chain management in the food business, and so on. AI can empower precision agricultureprecision agriculture; farmers can improve crop supervision techniques and increase productivity by getting real-time information on soil composition, moisture levels of soil, weather patterns and informed decisions about irrigation, pest management, as well as harvesting, etc. This chapter pronounces the current state of AI in the food industry, its benefits, demerits, and challenges and outlines the future trends in the food industry like augmented reality with AI and AI-driven smart packaging to improve and monitor food quality.
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
