I am here to upload my doctoral credit seminar on Artificial Intelligence to track plant diseases

1. DOCTORAL SEMINAR ON
ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASES
Presented by
Mr. Sabhavat Srinivasnaik
ID.No.RAD/2021-25
In-Service Ph.D I Year
Course In-charge
Dr.Bharati N. Bhat, Professor
Dept. of Plant Pathology
DEPARTMENT OF PLANT PATHOLOGY
COLLEGE OF AGRICULTURE, RAJENDRANAGAR
PROFESSOR JAYASHANKAR TELANGANA STATE AGRICULTURAL UNIVERSITY
Date:10.10.2022 Venue: Seminar Hall
Chairperson
Dr. K. Vijaya Lakshmi, Sr. Professor
Dept. of Entomology

2. Preamble
Artificial Intelligence
CONTENTS
AI in Agriculture & Disease tracking
Applications of Artificial Intelligence
References
Case studies
Recent trends in Artificial Intelligence
Summary and Conclusion
Future strategies

3. PREAMBLE

4. PREAMBLE
Population growth@9.8 billion by 2050 @192 mt-2000mt by 2030
Huge demand for food
Productivity should be increased
Prone to different diseases
Bacterial, Fungal and Viral diseases-10-95% loss
Early detection is crucial
Reduce excessive usage of pesticides
Traditional diagnostic methods
 Visual based
 Biochemical based
 Molecular based
Time, Processing and Analysis
Optical imaging techniques and hyperspectral imaging-Automatic-
Processing-Algorithms based-Disease detection
AI
Orchi,2022

5. ARTIFICIAL INTELLIGENCE

6. ARTIFICIAL INTELLIGENCE
The exciting new effort to make computers think, machines with
minds, in the full and literal sense (Haugeland, 1985)
AI is composed of two words Artificial and Intelligence, where
Artificial defines "Man-made" and intelligence defines “Thinking
power", hence AI means "a man-made thinking power”
 Booming technology of computer science
 Self-driving cars, Proving theorems, Playing
music & Painting etc.
 AI in Agriculture- Protection and Production
-Rusell and Norvig, 2009

7. HISTORY OF ARTIFICIAL INTELLIGENCE
Can machines think? – Alan Turing, 1950
Mohanty et al.,2016

8. Father of Artificial Intelligence-John McCarthy

9. ARTIFICIAL INTELLIGENCE
 We can create soft wares or devices which
can solve real world problems easily and with
accuracy
 Personal virtual Assistant, such as Cortana,
Google Assistant, Siri, etc.
 Robots which can work in an environment
where survival of humans can be at risk.
 AI opens a path for detection of plant
disease and their management
Hasan et al.,2019

10. TYPES OF ARTIFICIAL INTELLIGENCE
Orchi,2022

11. DOMAINS OF ARTIFICIAL INTELLIGENCE
Prabha, 2020

12. ALGORITHMS OF MACHINE LEARNING
Riya Patel,2022

13. DEEP LEARNING
 Deep learning is the process of implementing neural networks on
high dimensional data to gain insights and solutions
 It is an advanced field of machine learning that can be used to
solve advanced problems
 It consists of multi neural network architecture.
 Artificial Neural Network (ANN) :It is for the
data in the form of numbers
 Convolutional Neural Network (CNN):It is
for the data in the form of image. It is widely
used in Plant Pathology i.e. for image
processing
 Recurrent Neural Network (RNN):It is used
for Time and Series and its used in
developing forecast model
Riya Patel,2022

14. EXPERT SYSTEM
Expert system: It is an AI based computer system that learns and
reciprocates the decision making ability of a human expert

15. APPLICATIONS OF AI

16. APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Rusell and Norvig, 2009

17. AI IN AGRICULTURE

18. AI IN AGRICULTURE
AI in Plant Pathology
Rusell and Norvig, 2009

19.  Plant stress phenotyping using hyper spectral data interpretation
through computer vision is most widely used in disease
diagnostics.
 The optical properties of plants reflected from changes in physiology
due to biotic stresses envisaged as change in plant phenotype like
tissue colour change, leaf malformations, plant canopy change
are assessed through computer vision within different regions of
the electromagnetic spectrum.
 A large, verified dataset of images of diseased and healthy plants
are required to develop accurate image classifiers for disease
diagnosis.
AI IN DISEASE TRACKING
Prabha, 2020

20.  A leaf symptom recognition method based on visible spectrum image
processing with a high detection accuracy of 91.93% for citrus greening
 An automated system based on machine vision comprising two
convolutional neural networks to detect and distinguish Asian citrus
psyllid, the vector of greening disease with a precision of 80%.
 A hyper spectral (400–1000 nm) imaging system was used for the detection
of citrus canker on leaves and immature fruit at various disease
development stages
 Detection of strawberry powdery mildew, based on image texture and
classified with support vector machines (SVM) and k nearest neighbours
(kNN), showed that SVM classified PM disease with highest overall accuracy
of 91.86%
AI IN DISEASE TRACKING
Prabha, 2020

21. MECHANISM OF PLANT DISEASE TRACKING
Orchi,2022

22. Prabha, 2020
MECHANISM OF PLANT DISEASE TRACKING

23. ALGORITHMS FOR PLANT PATHOGENS
Prabha, 2020

24. PLANT VILLAGE DATASET
www.kaggle.com/13.9.2022

25. Kamdar et al.,2021

26. Kamdar et al.,2021

27. FACTORS EFFECTING AI ACCURACY
www.kaggle.com/13.9.2022
1. The data size-Epoch and Batch size
2. Type of image-Library/Real time
3. Expert system
4. Model/Algorithm

28. CASE STUDIES

29. CASE STUDY 1
1. Xanthomonas wilt of banana (BXW)
2. Fusarium wilt of banana (FWB)
3. Black sigatoka (BS)
4. Yellow sigatoka (YS)
5. Banana bunchy top virus (BBTV)
6. Banana corm weevil (BCW)
 Africa and Southern India
 CGIAR project
 2019
 International Center for Tropical Agriculture

30. METHODOLOGY
Selvaraj et al.,2019

31. METHODOLOGY
Selvaraj et al.,2019
ResNet50, InceptionV2 and MobileNetV1

32. RESULTS AND DISCUSSION
Selvaraj et al.,2019

33. RESULTS AND DISCUSSION
Selvaraj et al.,2019

35. CASE STUDY 2
 A hybrid network has been implemented by
combining deep convolutional neural
network (DCNN) with support vector
machine (SVM) to classify rice diseases
 Bangladesh in 2019
 9 diseases of paddy
 1080 images
 Hajee Mohammad Danesh Science &
Technology University
Hasan, J., Mahbub, S and Alom,S.
Objective:

39. CASE STUDY 3
 Science Park in the west campus of China
Agriculture University
 Vocational and Technical College of Inner
Mongolia Agricultural University
 There are 2735 normal images, 521 sheath
blight images, 459 rust images, and 713
northern leaf blight images, altogether 4428
images

40. METHODOLOGY
Zhang et al., 2021

41. RESULTS AND DISCUSSION
Zhang et al., 2021

42. RESULTS AND DISCUSSION
Zhang et al., 2021

43. RESULTS AND DISCUSSION
Zhang et al., 2021

44. CASE STUDY 4
1. Septoria leaf spot- Septoria lycopersici
2. Early blight of leaf spot-Alternaria solani
3. Late blight of leaf spot- Phytophthora infestans
4. Mosaic virus-ToMV

45. Patil et al., 2020
SYMPTOMS OF DIFFERENT DISEASES OF TOMATO

46. Patil et al., 2020
METHODOLOGY
Dataset -520 images
412×412 dimensions
10% testing
Image net
(MAP) is found to be 0.76.
High resolution
Light conditions

47. Patil et al., 2020
RESULTS AND DISCUSSION

48. CASE STUDY 5
 Grape (Vitis vinifera)
 Downy mildew and Powdery mildew
 Pune, Bijapur and Sangali during 2013

49. Sannaki et al., 2013
METHODOLOGY

50. Sannaki et al., 2013
RESULTS AND DISCUSSION

51. Sannaki et al., 2013
RESULTS AND DISCUSSION

52. Sannaki et al., 2013
RESULTS AND DISCUSSION

53. Kamdar et al.,2021
AI IN OTHER CROPS

54. IOTs
 It describes the network of physical objects-things that are embedded with
sensors, software, other technologies for the purpose of connecting and
exchanging data with other devices and systems over the internet
 IOT in agricultural context refers to the use of sensors, cameras and other
devices to turn every element and action involved in farming into data
 Large datasets on weather, Moisture, Plant health, mineral status, chemical
applications, pest detection
Kamdar et al.,2021

55. ROBOTICS
Robotics is a branch of Artificial Intelligence focusses on different branches of
science and having multiple applications
Agri-robo, Robotic sprayers and Strawberry harvesters
Orchi,2022

56.  Plant detectives and engineers from the University of Florida are using
artificial intelligence to find a disease early so growers who produce summer
squash can keep it under control.
 A lab- and UAV-based powdery mildew (PM) disease detection system was
developed

57. PJTSAU is the first State agricultural university
to bag the approval of SOPs by the Director-
General of Civil Aviation (DGCA) for the use of
drones in agricultural research.
57

58. AI APPS IN DISEASE TRACKING
 Plant doctor: A joint venture of Green savers and the United Nations
Volunteers (UNV)
 Rice Doctor: International Rice Research Institute (IRRI), Lucid team at the
University of Queensland, Australia
 Plantix: Progressive Environmental and Agricultural Technologies (PEAT),
Germany in collaboration with ICRISAT, CIMMYT, FAO and PJTSAU
 Crop Doctor: IGKV, Raichur, Chhattisgarh
 Leaf Doctor: Cornell University, University of Hawaii at Manoa, College of
Tropical Agriculture and Human Resources
 Rice xpert: ICAR-National Rice Research Institute (NRRI), Cuttack
 Pestoz: Identify Plant diseases-Agroconnect India Pvt.Ltd., Chattishgarh

59. OTHER AI APPS IN DIFFERENT COUNTRIES
Orchi,2022

60. OTHER AI APPS IN DIFFERENT COUNTRIES
Orchi,2022

61. AI INSTITUTIONS IN INDIA
 Robert Bosch Centre for Data Science and AI @ IIT Madras
 Centre of Excellence in Artificial Intelligence @ IIT Kharagpur
 Infosys centre for Artificial Intelligence @ IIIT Delhi
 NV AI Centre @ IIT Hyderabad
 Robert Bosch Centre for Cyber-Physical Systems @ IISc, Bangalore
 Centre of Excellence in Data Science at PEC Chandigarh
 Centre for Machine Intelligence and Data Science @ IIT Bombay
 Centre of Excellence in AI at NIT Trichy
 School of Artificial Intelligence @ IIT Delhi
 Centre for Artificial Intelligence and Data Science @ IIT Roorkee

62. SUMMARY & CONCLUSION

63. SUMMARY & CONCLUSION
Increasing human population needs high food production
Crop protection through AI is needed for accuracy, timely and effective
management
We are at a stage of Narrow AI
Deep learning is the advanced algorithm of disease tracking
Machine learning, Deep learning and Expert systems are important domains
used for Disease tracking
CNNs are the neural networks under deep learning used for disease tracking
Acquisition, Pre-processing, Segmentation, Feature extraction, Image
classification are the steps to detect the diseases
Datasets are useful libraries for disease detection : Plant village
Rice, Maize and Tomato are highly explored crops for usage of AI
Plantix app is one of AI based app for disease diagnosis
Indiaai is the Indian AI Database

64. FUTURE STRATEGIES

65. FUTURE STRATEGIES
Quantification of a plant disease
Mobile applications based on AI should reach the farming
community
Still better models needed to arrange the larger data set within
less time
The AI should able to detect the latent infections in the plants
Location specific AI models should be developed for the location
specific diseases

66. https://youtu.be/90SY5wAZdbc
A VIDEO ON AI FOR DETECTION OF
CASAVA DISEASES

67. REFERENCES
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Eltahir, Manar Ahmed Hamza and Abu Sarwar Zamani.2020. Artificial intelligence
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Gyan Singh Sujawata and Jitendra Singh Chouhan.2021.Application of Artificial
Intelligence in detection of diseases in plants: A Survey. Turkish Journal of
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Hasan, J., Mahbub, S and Alom,S.2019.Rice disease classification by combining deep
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68. REFERENCES
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69. REFERENCES
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70. THANK YOU