9 AI in plant growth promotion and plant disease management
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Nileema S. Gore
Abstract
Effective management of crops enhances productivity and development of agriculture. Plant diseases are a major cause for crop reduction. Early detection of disease not only enhances yield and quality but also reduces dependency on chemical pesticides. Artificial intelligence (AI) plays a significant role in disease detection and address challenges on fields. AI is used particularly in classifying and identifying diseases. Classification is the first step involving separating data into classes and detecting algorithms of machine learning (ML) and deep learning (DL). ML algorithms aim to allow computer to learn from experience; there are various subtypes of ML such as support vector machines, random forests, decision tree, and artificial neural network. AI and ML include DL, which has its influence on areas including natural language processing, recognition of objects, and classification of image. AI helps farmers by figuring out which crops will yield highest profits. With this analysis, farmers can reduce the failure of crops and business operations errors. AI can assist in the production of more disease-resistant and environmentally adaptable crops by gathering data on plant growth. AI systems can conduct chemical analysis of soil and produce information on the absent nutrients. AI helps forecast the best combination of agronomic products, find the best irrigation schedules, and time the application of nutrients. With AI, harvesting can be automated, and the ideal time for it may even be predicted. The use of ML to predict has the potential to remake entire sectors. The effectiveness of automatic plant disease detection and categorization is impacted by a few issues.
Abstract
Effective management of crops enhances productivity and development of agriculture. Plant diseases are a major cause for crop reduction. Early detection of disease not only enhances yield and quality but also reduces dependency on chemical pesticides. Artificial intelligence (AI) plays a significant role in disease detection and address challenges on fields. AI is used particularly in classifying and identifying diseases. Classification is the first step involving separating data into classes and detecting algorithms of machine learning (ML) and deep learning (DL). ML algorithms aim to allow computer to learn from experience; there are various subtypes of ML such as support vector machines, random forests, decision tree, and artificial neural network. AI and ML include DL, which has its influence on areas including natural language processing, recognition of objects, and classification of image. AI helps farmers by figuring out which crops will yield highest profits. With this analysis, farmers can reduce the failure of crops and business operations errors. AI can assist in the production of more disease-resistant and environmentally adaptable crops by gathering data on plant growth. AI systems can conduct chemical analysis of soil and produce information on the absent nutrients. AI helps forecast the best combination of agronomic products, find the best irrigation schedules, and time the application of nutrients. With AI, harvesting can be automated, and the ideal time for it may even be predicted. The use of ML to predict has the potential to remake entire sectors. The effectiveness of automatic plant disease detection and categorization is impacted by a few issues.
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
