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11 Artificial intelligence for plant disease detection: past, present, and future

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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

This chapter presents a discursive literature survey on the applications of artificial intelligence (AI) techniques in plant disease detection. The agriculture field faces many problems from cultivating to harvesting. Major concerns are various disease infections. This leads to severe yield loss with environmental hazards due to extreme usage of insecticides. With insane expansion of human population, the demand for food is incessantly surging. Conventional techniques used by farmers are not only adequate to satisfy the augmenting demand but also hamper the soil by intense use of hazardous pesticides. Besides, conventional techniques, AI gives many advantages in disease detection. In 1983, computer application was used to solve a problem in agriculture for the first time. Since then, numerous approaches have been designed to figure out a large number of problems in the field of agriculture. Furthermore, many databases and decision support systems have been developed. Out of these, AI techniques have been conveyed to deliver results with better accuracy and robustness. In addition, it enabled researchers to detect the complicated details of each condition and offer a solution that could be a perfect fit for the respective problem. Different AI techniques like convolutional neural network, artificial neural network, and deep learning have been successfully used for disease detection in rice, wheat, maize, cotton, tomato, peas, potato, cucumber, cassava, berries, peach, grapes, olives, mango, banana, apple, sweet paper, tea, and so on. This chapter discusses various AI techniques that were developed and used in agriculture for plant disease detection and discourses in its future to achieve precision in farming.

Abstract

This chapter presents a discursive literature survey on the applications of artificial intelligence (AI) techniques in plant disease detection. The agriculture field faces many problems from cultivating to harvesting. Major concerns are various disease infections. This leads to severe yield loss with environmental hazards due to extreme usage of insecticides. With insane expansion of human population, the demand for food is incessantly surging. Conventional techniques used by farmers are not only adequate to satisfy the augmenting demand but also hamper the soil by intense use of hazardous pesticides. Besides, conventional techniques, AI gives many advantages in disease detection. In 1983, computer application was used to solve a problem in agriculture for the first time. Since then, numerous approaches have been designed to figure out a large number of problems in the field of agriculture. Furthermore, many databases and decision support systems have been developed. Out of these, AI techniques have been conveyed to deliver results with better accuracy and robustness. In addition, it enabled researchers to detect the complicated details of each condition and offer a solution that could be a perfect fit for the respective problem. Different AI techniques like convolutional neural network, artificial neural network, and deep learning have been successfully used for disease detection in rice, wheat, maize, cotton, tomato, peas, potato, cucumber, cassava, berries, peach, grapes, olives, mango, banana, apple, sweet paper, tea, and so on. This chapter discusses various AI techniques that were developed and used in agriculture for plant disease detection and discourses in its future to achieve precision in farming.

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/10.1515/9783110691276-011

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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