1) Artificial intelligence can help detect and identify plant diseases by analyzing images of infected plants. Advanced techniques like machine learning and deep learning are being used for accurate disease identification. 2) IoT sensors are being used to monitor crop health and send data to the cloud for analysis. This allows early detection of diseases. 3) While AI has benefits like precision and speed, challenges remain in applying these new technologies at large scale in agriculture due to lack of familiarity, data and infrastructure requirements, and uncertainty around external growing conditions.

1. 1
Varsha
M.Sc., Plant
Pathology

2. INTRODUCTION
Agriculture is the principle foundation of
subsistence for about 58% of India’s population.
POPULATION ∞ FOOD
Quality and quantity of foods are degraded
due to disease infection.
Plant pests and diseases are the major
contributors to biotic stresses that limit
realization of yield potential of crop plants.
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3. Various laboratory approaches have been employed
for disease identification.
Advanced methods of plant disease
detection were introduced.
AI is introduced to detect and identify disease
on agricultural products.
Intervening of AI in Agriculture is serving farmers to recover their farming efficiency and
diminish environmental hostile influences- Anurag Saxena et al. (2020).
Earlier disease detection was completely dependent
on manual methods as farmers use their own
experience for detecting symptoms.
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4. WHAT IS ARTIFICIAL
INTELLIGENCE???......
Simulation of human intelligence
processes by machine.
Ability of a computer program or a
machine to think and learn.
Make computers “SMART”.
Work on their own without
encoded with commands.
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5. 1 ADAPTABILITY
SPEEDY PERFORMANCE
PRECISION
COST-VIABILITY
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6. FIELD OF ARTIFICIAL INTELLIGENCE IN
AGRICULTURAL SECTOR
6

7. AI powered enable a farmer “TO DO MORE WITH
LESS”
AI technology is supporting different sectors in agriculture
to boost productivity and efficiency.
Minimal environmental cost and maximize the
capacity.
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8. IoT DRIVEN DEVELOPMENT
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9. CROP HEALTH MONITORING
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10. IMAGE- BASED INSIGHT GENERATION.
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11. DISEASE DETECTION
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12. FIELD MANAGEMENT
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13. ROBOTICS IN AGRICULTURE
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14. AI IN DETECTIONANDIDENTIFICATION OF PLANT
DISEASES.
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15. ARTIFICIAL INTELLIGENCE
AI
ML
(Machine
learning)
DL
(Deep
learning)
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16. MACHINE LEARNING
AI application for the diagnosis of diseases.
ML Algorithms are fast and accurate.
“MACHINE LEARNS FROM PAST ENCOUNTERS”
Learning can be classified as:
 Supervised.
 Unsupervised.
 Semi-supervised.
 Reinforcement.
Gyan Singh Sujawat (2021)
16

17. WHAT IS
MACHINE
LEARNING???....
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19. DEEP LEARNING
Powerful machine learning approach
Overcomes complexity of the environment.
Improve the accuracy of identification.
Uses
“ARTIFICIAL NEURAL NETWORKS”.
Yan Guo et al. 2020 19

20. EVOLUTION OF DEEP LEARNING
MODELS FROM 2012
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21. DEEP LEARNING MODELS
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22. IMPLEMENTATION OF DEEP LEARNING
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NEURAL NETWORK

24. Manikanta munangi.,2019

25. RESEARCH ON DATABASE METHODOLOGY RESULTS
Grape- Powdery
mildew.
Apple- Apple scab.
Captured natural
images
K means clustering,
ANN.
90% accuracy
Soybean- brown spot 120 camera images SVM 93.79% accuracy
Citrus- canker,
anthracnose
200 image sample
training dataset and 50
as testing
GLCM 96% accuracy
Mukesh Kumar Tripathi 2019

26. DL Architectures/
Algorithms
Datasets Selected plants Accuracy
CNN Plant village Maize 92.85%
AlexNet,
GoogLeNet, ResNet
Plant village Tomato (97.28 %)
LeNet Plant village Banana (98.64 %)
AlexNet,
GoogLeNet,
Overfeat, VGG
Plant village and in-
field images
Apple, Blueberry, (99.53 %) which is
the best among all
AlexNet, VGG 16,
VGG 19,
SqueezeNet,
GoogLeNet,
Inceptionv3
Real field dataset Peach, Cherry 97.86± 1.56
Muhammad Hammad Saleem et al. 2019

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30. SUPPORTIVE ARTICLE

32. SUPPORTIVE ARTICLE
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Fig 2 – Sample images of normal and diseased leaves.

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38. HUMAN INTERVENTION
HARDWARE
TIME
APPROACH
APPLICATIONS
38

39. IMAGE
PROCESSING
Priyanka L. Kamble et al.(2019)
IMAGEACQUISITION
IMAGEPREPROCESSING IMAGESEGMENTATION
FEATURE
EXTRACTION
CLASSIFICATION
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40. IMAGE
ACQUISITION
( a ) ( b )
( c ) ( d )
A – Bacterial Blight
B – Cercospora Leaf Spot
C – Powdery Mildew
D - Rust

41. (a ) (b )
IMAGE
PREPROCESSING
QUALITY
NOISE
ENHANCEMENT
CROPPING
RGB FORMAT

42. ( b )
( c ) ( d )
IMAGE
SEGMENTATION
Dividing the image into
sub images.
Masking of green
colour.
Select the cluster
images.

43. FEATURE
EXTRACTION
CLASSIFICATION
COLOR FEATURES.
DISEASED
HEALTHY
SHAPE FEATURES.
TEXTURE FEATURES.

44. ACCURACY AND ERROR
COMPARISON
Yin Min Oo et al. (2019)
0%
10%
20%
30%
40%
50%
60%
Cercospora LEAF
SPOT
BACTERIAL
BLIGHT
POWDERY
MILDEW
RUST
ERROR
RATE
%
PLANT LEAF DISEASES
ERROR RATE COMPARISON
SVM CLASSIFIER KNN CLASSIFIER ENSEMBLE CLASSIFIER
0%
20%
40%
60%
80%
100%
120%
Cercospora
LEAF SPOT
BACTERIAL
BLIGHT
POWDERY
MILDEW
RUST
ACCURACY
%
PLANT LEAF DISEASES
ACCURACY COMPARISON
SVM CLASSIFIER KNN CLASSIFIER ENSEMBLE CLASSIFIER

45. IoT IN PLANT
DISEASE
DETECTION
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46. INFORMATION OBTAINING
TEMPERATURE SENSOR
COLOR SENSOR
AURDINO
CLOUDSTORAGE
46

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Fig1. ArchitectureofPrototype
Muhammad Amir Nawaz. (2020)

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Fig2. Circuit diagram of model

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SUPPORTIVE ARTICLE

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Apeksha thorat et.al 2017

52. TOOLSANDTECHNIQUES
52

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55. CHALLENGES IN AI ADOPTION IN
AGRICULTURE
Lacks familiarity with robust high tech technology
55
Vast agricultural land.
Lot of data is required to train the machines.
Uncertainty of external factors.

56. FUTURE ASPECT
India’s population is expected to reach more than 1.6
billion by 2030.
With this huge hike in population, one can expect
massive demand for agricultural consumption as well.
AI must be implemented to reduce crop diseases and
make it more efficient for farmers.
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57. CONCLUSION
AI can help farmers to increase the capacity of
production.
“Toeradicateextreme povertyand hunger”
57
Brings an ideal shift in way we do research and
development.
Automation with accuracy to perform real time disease
detection.

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