10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology
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Nitish Kumar Singh
Abstract
Prediction of plant disease epidemiology is being boosted by artificial intelligence (AI) and machine learning (ML). This strongly enhances crop productivity and sustainability of livelihood. Earlier, simple AI and ML models were used to monitor environmental factors and historical data to explain the health of crop plants. The advancements of instruments and imaging technologies, which are used in weather forecasting and analysis of soil, are now unearthing plant health and disease outbreaks. Of late, convolutional neural networks and deep learning models are extensively used modern information technology tools that enable high-resolution images with higher accuracy to identify the onset of plants’ illness. These high-throughput models prompt to develop paramount solutions for disease management and improvement in productivity of crop plants species. Furthermore, AI-driven models are useful to reveal the disease frequency and dissemination in plants under fluctuating environmental conditions, which offer vital approaches for sustainable and climate-smart agricultural practices. AI and ML algorithms are used to analyze data for the procurement of information obtained from various sources, which improves accuracy to permit real-time decision-making ability during the prevalence of plant diseases. Eventually, AI and ML are ideal tools in plant disease epidemiology to improve crop health management and increase food security, which is fundamental for precision agriculture.
Abstract
Prediction of plant disease epidemiology is being boosted by artificial intelligence (AI) and machine learning (ML). This strongly enhances crop productivity and sustainability of livelihood. Earlier, simple AI and ML models were used to monitor environmental factors and historical data to explain the health of crop plants. The advancements of instruments and imaging technologies, which are used in weather forecasting and analysis of soil, are now unearthing plant health and disease outbreaks. Of late, convolutional neural networks and deep learning models are extensively used modern information technology tools that enable high-resolution images with higher accuracy to identify the onset of plants’ illness. These high-throughput models prompt to develop paramount solutions for disease management and improvement in productivity of crop plants species. Furthermore, AI-driven models are useful to reveal the disease frequency and dissemination in plants under fluctuating environmental conditions, which offer vital approaches for sustainable and climate-smart agricultural practices. AI and ML algorithms are used to analyze data for the procurement of information obtained from various sources, which improves accuracy to permit real-time decision-making ability during the prevalence of plant diseases. Eventually, AI and ML are ideal tools in plant disease epidemiology to improve crop health management and increase food security, which is fundamental for precision agriculture.
Chapters in this book
- 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
Chapters in this book
- 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
