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1 Smart farming: using IoT and machine learning techniques

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Internet of Things and Machine Learning in Agriculture
This chapter is in the book Internet of Things and Machine Learning in Agriculture

Abstract

Internet of things (IoT) and machine learning (ML) together have a great impact on each domain. The agriculture domain is not an exception to it as it helps in the transformation of old-fashioned farming practice to smart farming. As we all know, the global population is increasing very fast and will be about 9.8 billion by 2050, and there is a drastic fluctuation in the weather around the world due to global warming. In order to feed this massive population in this harsh environmental condition, food productivity must be increased. So, there is a need to adopt smart farming in the agricultural industry. In this chapter, initially, we introduce the IoT architecture and the various protocols used to perform the data exchange between connected devices. Then we discuss about what ML is and its various categories. At the end of the chapter, we address the various challenges farmers face with the traditional method of farming and how smart farming powered by IoT and ML will improve the agriculture operations from the sowing of seeds to till harvesting of the crop.

Abstract

Internet of things (IoT) and machine learning (ML) together have a great impact on each domain. The agriculture domain is not an exception to it as it helps in the transformation of old-fashioned farming practice to smart farming. As we all know, the global population is increasing very fast and will be about 9.8 billion by 2050, and there is a drastic fluctuation in the weather around the world due to global warming. In order to feed this massive population in this harsh environmental condition, food productivity must be increased. So, there is a need to adopt smart farming in the agricultural industry. In this chapter, initially, we introduce the IoT architecture and the various protocols used to perform the data exchange between connected devices. Then we discuss about what ML is and its various categories. At the end of the chapter, we address the various challenges farmers face with the traditional method of farming and how smart farming powered by IoT and ML will improve the agriculture operations from the sowing of seeds to till harvesting of the crop.

Chapters in this book

  1. Frontmatter I
  2. Preface VII
  3. Acknowledgments IX
  4. Contents XI
  5. List of contributors XIII
  6. Part I: Machine learning and Internet of things in agriculture
  7. 1 Smart farming: using IoT and machine learning techniques 3
  8. 2 Food security and farming through IoT and machine learning 21
  9. 3 An innovative combination for new agritechnological era 41
  10. 4 Recent advancements and challenges of artificial intelligence and IoT in agriculture 65
  11. 5 Technological impacts and challenges of advanced technologies in agriculture 83
  12. Part II: Applications of Internet of things in agriculture
  13. 6 IoT-based platform for smart farming – Kaa 109
  14. 7 Internet of things platform for smart farming 131
  15. 8 Internet of things platform for smart farming 159
  16. 9 Internet of things platform for smart farming 169
  17. Part III: Applications of machine learning in agriculture
  18. 10 Kisan-e-Mitra: a tool for soil quality analyzer and recommender system 205
  19. 11 Artificial intelligence for plant disease detection: past, present, and future 223
  20. 12 Wheat rust disease identification using deep learning 239
  21. 13 Image-based hibiscus plant disease detection using deep learning 251
  22. 14 Rainfall prediction by applying machine learning technique 275
  23. 15 Plant leaf disease classification based on feature selection and deep neural network 293
  24. 16 Using deep learning for image-based plant disease detection 323
  25. 17 Using deep learning for image-based plant disease detection 355
  26. 18 Using deep learning for image-based plant disease detection 369
  27. Index 403
https://doi.org/10.1515/9783110691276-001

Chapters in this book

  1. Frontmatter I
  2. Preface VII
  3. Acknowledgments IX
  4. Contents XI
  5. List of contributors XIII
  6. Part I: Machine learning and Internet of things in agriculture
  7. 1 Smart farming: using IoT and machine learning techniques 3
  8. 2 Food security and farming through IoT and machine learning 21
  9. 3 An innovative combination for new agritechnological era 41
  10. 4 Recent advancements and challenges of artificial intelligence and IoT in agriculture 65
  11. 5 Technological impacts and challenges of advanced technologies in agriculture 83
  12. Part II: Applications of Internet of things in agriculture
  13. 6 IoT-based platform for smart farming – Kaa 109
  14. 7 Internet of things platform for smart farming 131
  15. 8 Internet of things platform for smart farming 159
  16. 9 Internet of things platform for smart farming 169
  17. Part III: Applications of machine learning in agriculture
  18. 10 Kisan-e-Mitra: a tool for soil quality analyzer and recommender system 205
  19. 11 Artificial intelligence for plant disease detection: past, present, and future 223
  20. 12 Wheat rust disease identification using deep learning 239
  21. 13 Image-based hibiscus plant disease detection using deep learning 251
  22. 14 Rainfall prediction by applying machine learning technique 275
  23. 15 Plant leaf disease classification based on feature selection and deep neural network 293
  24. 16 Using deep learning for image-based plant disease detection 323
  25. 17 Using deep learning for image-based plant disease detection 355
  26. 18 Using deep learning for image-based plant disease detection 369
  27. Index 403
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