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17 Using deep learning for image-based plant disease detection
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Yash Joshi
Abstract
Crop ailments pose a great threat to us. Crops are a major source of nutrition in the world. It is usually very hard to identify and analyze ailments in a crop through the naked eye. In this chapter, we discuss how different deep learning and machine learning techniques can be used to identify and analyze crop aliments with high accuracy.
You are currently not able to access this content.
Abstract
Crop ailments pose a great threat to us. Crops are a major source of nutrition in the world. It is usually very hard to identify and analyze ailments in a crop through the naked eye. In this chapter, we discuss how different deep learning and machine learning techniques can be used to identify and analyze crop aliments with high accuracy.
You are currently not able to access this content.
Chapters in this book
- Frontmatter I
- Preface VII
- Acknowledgments IX
- Contents XI
- List of contributors XIII
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Part I: Machine learning and Internet of things in agriculture
- 1 Smart farming: using IoT and machine learning techniques 3
- 2 Food security and farming through IoT and machine learning 21
- 3 An innovative combination for new agritechnological era 41
- 4 Recent advancements and challenges of artificial intelligence and IoT in agriculture 65
- 5 Technological impacts and challenges of advanced technologies in agriculture 83
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Part II: Applications of Internet of things in agriculture
- 6 IoT-based platform for smart farming – Kaa 109
- 7 Internet of things platform for smart farming 131
- 8 Internet of things platform for smart farming 159
- 9 Internet of things platform for smart farming 169
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Part III: Applications of machine learning in agriculture
- 10 Kisan-e-Mitra: a tool for soil quality analyzer and recommender system 205
- 11 Artificial intelligence for plant disease detection: past, present, and future 223
- 12 Wheat rust disease identification using deep learning 239
- 13 Image-based hibiscus plant disease detection using deep learning 251
- 14 Rainfall prediction by applying machine learning technique 275
- 15 Plant leaf disease classification based on feature selection and deep neural network 293
- 16 Using deep learning for image-based plant disease detection 323
- 17 Using deep learning for image-based plant disease detection 355
- 18 Using deep learning for image-based plant disease detection 369
- Index 403
Chapters in this book
- Frontmatter I
- Preface VII
- Acknowledgments IX
- Contents XI
- List of contributors XIII
-
Part I: Machine learning and Internet of things in agriculture
- 1 Smart farming: using IoT and machine learning techniques 3
- 2 Food security and farming through IoT and machine learning 21
- 3 An innovative combination for new agritechnological era 41
- 4 Recent advancements and challenges of artificial intelligence and IoT in agriculture 65
- 5 Technological impacts and challenges of advanced technologies in agriculture 83
-
Part II: Applications of Internet of things in agriculture
- 6 IoT-based platform for smart farming – Kaa 109
- 7 Internet of things platform for smart farming 131
- 8 Internet of things platform for smart farming 159
- 9 Internet of things platform for smart farming 169
-
Part III: Applications of machine learning in agriculture
- 10 Kisan-e-Mitra: a tool for soil quality analyzer and recommender system 205
- 11 Artificial intelligence for plant disease detection: past, present, and future 223
- 12 Wheat rust disease identification using deep learning 239
- 13 Image-based hibiscus plant disease detection using deep learning 251
- 14 Rainfall prediction by applying machine learning technique 275
- 15 Plant leaf disease classification based on feature selection and deep neural network 293
- 16 Using deep learning for image-based plant disease detection 323
- 17 Using deep learning for image-based plant disease detection 355
- 18 Using deep learning for image-based plant disease detection 369
- Index 403
