5 Technological impacts and challenges of advanced technologies in agriculture
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Sivakumar Rajagopal
Abstract
In the present world, agriculture takes a vital part of the economy of many developing and developed nations through the production of food materials, generation of income, and industrial development. Thus, it has been considered as an essential and foremost sector worldwide. United Nation’s Agriculture Organization (FAO) forecasts that the community of the world may reach up to 8 billion by 2025 and 9.6 billion by 2050. Primarily, this can be related to an increase in the production of food materials to around 70%, which must be achieved by 2050 worldwide. Globally, the cultivation of crops is being hindered by several biotic and abiotic factors, which significantly reduce the production and productivity of several economically important plants. Thus, the development of effective production and protection technologies is crucial to bring the maximum output. The recent advent of modern technologies, including the Internet of things (IoT) and machine learning (ML), has a high impact on agriculture. They are enabling agriculture to utilize data operated, directing to the more precise and profitable making of food materials through effective utilization of water and nutrient resources. The progress of ML and IoT has supported researchers to implement these methods in crop production (quality and quantity assessment), protection (identification of pest and disease), management (soil and water management), and livestock production and management which would enhance the production and productivity of crops and economic status of the farmers. This chapter highlights an overview of the modern technologies deployed to agriculture and proposes an abstract of the present and possible applications, and elaborates the challenges and suitable explanations and execution. Lastly, it presents some future directions for the IoT applied in the agriculture domain using ML and IoT.
Abstract
In the present world, agriculture takes a vital part of the economy of many developing and developed nations through the production of food materials, generation of income, and industrial development. Thus, it has been considered as an essential and foremost sector worldwide. United Nation’s Agriculture Organization (FAO) forecasts that the community of the world may reach up to 8 billion by 2025 and 9.6 billion by 2050. Primarily, this can be related to an increase in the production of food materials to around 70%, which must be achieved by 2050 worldwide. Globally, the cultivation of crops is being hindered by several biotic and abiotic factors, which significantly reduce the production and productivity of several economically important plants. Thus, the development of effective production and protection technologies is crucial to bring the maximum output. The recent advent of modern technologies, including the Internet of things (IoT) and machine learning (ML), has a high impact on agriculture. They are enabling agriculture to utilize data operated, directing to the more precise and profitable making of food materials through effective utilization of water and nutrient resources. The progress of ML and IoT has supported researchers to implement these methods in crop production (quality and quantity assessment), protection (identification of pest and disease), management (soil and water management), and livestock production and management which would enhance the production and productivity of crops and economic status of the farmers. This chapter highlights an overview of the modern technologies deployed to agriculture and proposes an abstract of the present and possible applications, and elaborates the challenges and suitable explanations and execution. Lastly, it presents some future directions for the IoT applied in the agriculture domain using ML and IoT.
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
