2 Food security and farming through IoT and machine learning
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Ashish Tripathi
Abstract
Agriculture plays a vital role in the Indian socioeconomy. In 1871, the Department of Agriculture and Commerce was started by Lord Mayo, the fourth viceroy of India, and A. O. Hume. On the basis of Famine Commission reports of 1880, 1898, and 1900, respectively, the government identified and set up a Department of Agriculture. In 1905, the Agriculture Research Institute became the Indian Agricultural Research Institute (IARI). From the IARI, the green revolution stemmed. After independence, the main challenge has been to generate enough healthy food with high nutrition for the Indian population. Article 47 states that public health with increased nutrition and standard of living is the first duty of the state, and thus the National Food Security Act 2013 has become a high priority of the government. Therefore, the varieties of high yielding crops were promoted in conjunction with excess use of chemical fertilizers, pesticides, and irrigation without knowing the negative impact on future farming and soil health. In recent years, some fruitful initiatives like the usage of innovative technologies and positive government policies have been taken in the agricultural sector to maximize the overall production rate with the required quality of soil and minimize the input cost. But, due to continuous growth in population, there is a huge need to produce nutrition-enriched crops to fulfill the hunger as well as maintain the soil health by promoting the use of biofertilizers and green manure, and controlled use of irrigation as per the necessity. In this chapter, our focus is to discuss a long-term strategy by incorporating research and innovation for a sustainable agricultural system based on technologies such as the Internet of things and machine learning that can play a significant role to advance sustainable farming and food nutrition. This may include methods to improve the soil fertility, to optimize the use of water (more crops per drop), to enhance farmers’ well-being, to study the effect of weather changes on soil fertility, to strengthen social equity and local economy, and to promote the use of biofertilizers and green manure.
Abstract
Agriculture plays a vital role in the Indian socioeconomy. In 1871, the Department of Agriculture and Commerce was started by Lord Mayo, the fourth viceroy of India, and A. O. Hume. On the basis of Famine Commission reports of 1880, 1898, and 1900, respectively, the government identified and set up a Department of Agriculture. In 1905, the Agriculture Research Institute became the Indian Agricultural Research Institute (IARI). From the IARI, the green revolution stemmed. After independence, the main challenge has been to generate enough healthy food with high nutrition for the Indian population. Article 47 states that public health with increased nutrition and standard of living is the first duty of the state, and thus the National Food Security Act 2013 has become a high priority of the government. Therefore, the varieties of high yielding crops were promoted in conjunction with excess use of chemical fertilizers, pesticides, and irrigation without knowing the negative impact on future farming and soil health. In recent years, some fruitful initiatives like the usage of innovative technologies and positive government policies have been taken in the agricultural sector to maximize the overall production rate with the required quality of soil and minimize the input cost. But, due to continuous growth in population, there is a huge need to produce nutrition-enriched crops to fulfill the hunger as well as maintain the soil health by promoting the use of biofertilizers and green manure, and controlled use of irrigation as per the necessity. In this chapter, our focus is to discuss a long-term strategy by incorporating research and innovation for a sustainable agricultural system based on technologies such as the Internet of things and machine learning that can play a significant role to advance sustainable farming and food nutrition. This may include methods to improve the soil fertility, to optimize the use of water (more crops per drop), to enhance farmers’ well-being, to study the effect of weather changes on soil fertility, to strengthen social equity and local economy, and to promote the use of biofertilizers and green manure.
Kapitel in diesem Buch
- 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
Kapitel in diesem Buch
- 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
