Digital transformation in plant protection leads to o Increased efficiency: Reduced manual labour, operational costs, improved resource allocation, and optimised workflows. o Data driven decision making: Farmers can make more informed choices based on data-driven insights, leading to better pest and disease management strategies. o Automation and predictive analytics: Automation of tasks like pesticide application has reduced human error and resource waste. Predictive analytics models optimise preventive measures. o Monitoring: Digital solutions enable real-time monitoring by using cell phones. o Knowledge sharing and innovation: Rapid sharing of knowledge, best practices, and information among farmers, researchers, and stakeholders is possible.  Also, digital transformation opens up avenues for communication among farmers, scientists, and government bodies, resulting in a multitude of indirect benefits: scientists gain better data access, governments improve their policy-making processes, and farmers attain increased crop productivity.

1. DOCTORAL SEMINAR-2
Seminar: Dr. G.K Mahapatra
Incharge
SPEAKER: Prajwal Gowda M.A
(RN: 12292)
DIGITAL
TRANSFORMATION IN
PLANT PROTECTION

2. 1 Introduction
2 Artificial Intelligence
3 GPS, GIS, Remote Sensing & Precision
Agriculture
4
Mobile, Web Applications & Social
Media
5 Case Studies & Conclusion
CONTENTS

3.  Agriculture, an essential consideration of any country, is approximated that over
820 million people are in hunger today (FAO, 2020). Furthermore, with the
global population expected to reach 9.1 billion in 2050, 70% more food needs to
be produced.
 India ranks 107th out of 121 countries on Global Hunger Index 2022.
 With only 2.4 percent of the world’s total land area India has to support 14
percent of the world’s total population.
 The number of Indians at risk from hunger in 2030 is expected to be 73.9 million
 Hence, digital transformation in Indian agriculture is essential to enhance
efficiency, productivity and sustainability.
INTRODUCTION

4. Digital
Transformation
Is applying digital technologies to impact all aspects of
farming/business.
Is the economic and social effects of digitization &
digitalization.
Digitization Process of converting information into a computer readable format.
Digitalization
Is the use of digital technologies & data as well as interconnection
that results in new or changes to existing activities.
Digital Technologies Are the electronic tools, systems, devices and resources that generate,
store or process data such as mobiles, social media etc.
Senaras and Sezen, 2020

5. Key Drivers of Digital Transformation in India Rajak, 2023

6. Artificial intelligence

7.  It is a branch of computer science which deals with the simulation of human
intelligence processes by computer systems such as problem-solving, learning and
decision-making.
 AI possesses the capability to learn from data, thus identify the patterns in the data
more efficiently than humans, enabling researchers to gain more insight.
 AI aims to create intelligent machines that can think and function like humans.
 Term “Artificial Intelligence” was first introduced in the 1955 by John McCarthy.
 The application of AI in agriculture was first attempted by McKinion and Lemmon
in 1985 to create GOSSYM, a cotton crop simulation model using Expert System to
optimize cotton production (Gertsis et al., 1997).
ARTIFICIAL INTELLIGENCE

8. Artificial intelligence
Machine
learning
Deep
learning
Ability of machine to
imitate intelligent human
behavior
Application of AI that
allows a system to
automatically learn
and improve from
experience
Application of Machine
Learning that use complex
algorithms and deep neural
nets to train a model
Domains of Artificial Intelligence
Latif et al., 2019

9. Developments in Artificial Intelligence
Awais Bajwa, 2019

10. AI techniques for Crop Protection
 It is a subset of AI which is concerned with the design and development of algorithms and
statistical models that enable computers to evolve behaviour based on empirical data. ML
analyze data from sensors, satellites, and drones to detect patterns related to pests, diseases,
and crop health. They can predict potential issues and recommend appropriate actions.
Example: Classification of diseased or non-diseased leaves, fruit, plants, etc.
1. Machine learning (Learning from Experience/Predictive Analytics)
Das et al., 2022

11. 2. Deep Learning
 Deep learning is a subset of machine learning that employs
artificial neural networks that learn by processing data.
 Artificial neural networks mimic the biological neural networks in
the human brain.
 Deep learning models can analyze images of crops to detect
diseases and pests by logical functioning.
 It provide a hierarchical representation of the data by means of
various convolutions.
 A strong advantage of DL is feature learning, i.e., the automatic
feature extraction from raw data.
 To improve feature extraction, neural networks are integrated with
various image pre-processing algorithms.
Li et al., 2021

12. A Convolutional Neural Network (CNN) is a type
of Deep Learning neural network architecture
commonly used in Computer Vision. Computer
vision is a field of Artificial Intelligence that
enables a computer to understand and interpret the
image or visual data.
CNN are used to perform tasks such as crop
disease detection, yield prediction, and pest
identification.
Examples of CNN used in Plant Protection:
AlexNet: This breakthrough came in 2012, invented
by Krizhevsky et al. It involves 1.3 million images
divided into 1,000 categories & it is known for its
deep layers and efficient feature extraction.
YOLO (You Only Look Once):YOLO is a real-time
object detection system often used for identifying
pests or anomalies in crop fields.
Kittichai et al., 2021

13. 3. Image processing techniques
There are 5 types of image processing:
1. Visualization - Find objects that are not visible in the image
2. Recognition - Distinguish or detect objects in the image
3. Sharpening and restoration - Create an enhanced image from the original image
4. Pattern recognition - Measure the various patterns around the objects in the image
5. Retrieval - Browse and search images from a large database of digital images that are
similar to the original image
Example: Image based pest and disease identification
For boosting the efficiency of illness diagnosis, several pre-processing techniques such
as picture clipping, image smoothing, and image enhancement are used.
Ramaiah et al., 2023

14.  NLP focuses on enabling computers to understand, interpret and generate human language.
 NLP can be used to analyze text data from agricultural reports, weather data, scientific
papers or social media to identify trends and outbreaks of pests and diseases.
 It can be used in chatbots or virtual assistants to answer farmers' queries and provide
information on crop protection measures.
 NLP can assist in developing expert systems that process textual descriptions of crop
symptoms and provide rapid identification of diseases or pests.
 NLP can help integrate data from various sources, including satellite imagery, weather data.
EXAMPLES OF DATA SETS FOR NLP
⁕ Wikipedia Dump, GPT-2 Dataset, Quora Question Pairs
4. Natural Language Processing
Hegde and Patil, 2020

15. APPLICATION OF ARTIFICIAL INTELLIGENCE
Data Sets for training Artificial Intelligence: It is the collection of data that is needed to train
the model and make predictions.
Examples: For Computer Vision- ImageNet, COCO, MNIST
For Autonomous Vehicles- nuScenes & KITTI Vision Benchmark suite
Misra, 2019

16. Plastic Picusan Type Trap (Optoelectronic
detector embedded)
Left: Optoelectronic sensor Right:
Microcontroller counting light interruptions &
message delivery
Detection device mounted on funnel

17. Online web interface presenting
detected counts of pests Mean counts of RPW in an electronic

18. DIRT Smart
1. Automated identification (and count) of the Bactrocera oleae (Rossi) based on images of the
commonly used McPhail trap’s contents.
2. Smart-traps, feature a camera taking pictures of the pests collected by the trap that are then
examined.
3. The detection models provided were pre-trained on the COCO, KITTI and Open Images
datasets.
Flowchart of DIRT’s Creation &

19. Sample images from
dataset with label
Detection models comparison after training on
fruit fly image dataset

20. Examples of insect classes in NBAIR
Three insect datasets were used in the
study:
• NBAIR dataset: Consisting of 40 classes
of field crop insect images.
• Xie1 dataset: Containing 24 classes of
insects.
• Xie2 dataset: Comprising 40 classes of
insects.
The proposed CNN model was compared
with several pre-trained deep learning
architectures, including AlexNet, ResNet,
GoogLeNet, and VGGNet, for insect
classification.

21. Deep CNN Model
Architecture
Overall Classification

22. GPS: Global Positioning System
 It is a constellation of 24 satellites. It provides information related to location of an object
or area to the users using a ground based antenna and receiver, with the help of signal
collected from the satellites
 GPS is widely used in many applications related to surveying and navigation.
 GPS is a navigation system based on a network of earth orbiting satellites that let users
record near instantaneous positional information (latitude, longitude and elevation) with
accuracy ranging from 100m to 0.01m.
 GPS-equipped drones or ground vehicles can be used to collect data on pests or
diseases.
Featherstone, 1995

23. GIS: Geographical Information System
 It takes data collected from many sources in many forms as input and converts it
into information depending the process adopted by the user.
 GIS provide information related to geographic data. It is widely used for preparing
different types of maps in cartographic studies and also in environmental
applications etc.
 GIS is a computer system capable of assembling, storing, manipulating and displaying
geographically referenced information.
 GIS technology allows to store field input and output data as separate map layers in a digital
map and to retrieve and utilize these data for future input allocation decisions.
Hu, 2010

24. Geographic Information System (GIS)
GIS links geographic information (where things are) with
descriptive information (what things are).
GIS = G + IS
Where ? Geographic
reference
+ Information
System
What ?
Spatial coordinates
(longitude, latitude) of
locations on the surface of
the earth (spatial data)
Database
(attribute data of
locations)
All attribute data in GIS
must be linked to a
geographic reference
GIS consists of:
• Spatial information of
coordinates
• Data-base of attributes
• Some way to link the two
Hill, 2009

25. From map to GIS: The Structure of GIS (abstracting the real world into layers)
Attribute
Data
Tables
(MS
Access)
information
attached to
each layer
• GIS abstracts world into layers of spatial and
attribute data – one layer for one feature/
theme
• Different themes are brought
together using layers
Spatial data (points, lines, areas)
FAO, 2006

26. Applications of GPS & GIS in plant protection
1. Habitat Susceptibility Assessment:
•Mapping Habitat Features: GPS and GIS can be used to map and digitize habitat features
such as vegetation types, topography, and land use. This spatial data helps identify areas
susceptible to pest or disease outbreaks based on environmental factors.
•Data Integration: GIS can integrate spatial data on environmental factors like temperature,
humidity, land cover, and soil composition. Combining this with GPS data helps in assessing
which environmental conditions are conducive to disease or pest propagation.
•Identifying hotspots: GIS allows the overlaying of historical pest or disease outbreak data
with current environmental conditions to identify patterns and predict future outbreaks.
2. Census Data Compilation:
•Precise Enumeration: GPS provides precise coordinates for locations in the field.
•Spatial Analysis & Temporal Analysis: : GIS enables the integration of census data with
geographic features. Over time, GIS can help identify population trends, migration patterns,
and changes in land use, all of which are valuable for policymakers and researchers.
Liebhold et al., 1993

27. 3. Precision Agriculture:
 GPS and GIS technologies enable farmers to map their fields accurately and analyze data about soil
quality, moisture levels, and crop health.
 Monitoring of changes in crop and soil attributes and the identification of signs of pest damage
within a field.
 This information is then transformed into spatial maps that provide insights into the field's variability,
enabling targeted management interventions hence optimizing the use of pesticides.
4. Early Warning Systems: GPS and GIS can be integrated into early warning systems for plant
protection. Farmers and agricultural authorities can receive alerts and recommendations based on real-
time data about weather conditions, pest migrations, and disease outbreaks.
5. Decision Support Systems: GIS-based decision support systems provide farmers with valuable
information for making informed decisions about when and where to apply treatments, helping to
optimize pesticides allocation.
Sood et al., 2015

28. Insect census data and GIS
• Spears et al.(1991) and Ravlin et al (1991) used a GIS to interpolate gypsy moth trap
countsandeggmass densitiesinan IPM demonstrationprogram.
• They showed how map compilations of these data are useful for planning suppression
activities.

29. Remote Sensing
 Remote sensing is acquisition of information
about an object or phenomenon without
making physical contact with the object.
 It is the process of detecting and monitoring
the physical characteristics of an area by
measuring its reflected and emitted radiation
at a distance.
 The reflectance/ emittance of any object at
different wavelengths follow a pattern which
is characteristic of that object, known as
spectral signature.
 Proper interpretation of the spectral signature
leads to identification of the object. 17
Image acquisition by a
Rani, 2018

30. Spectral reflectance curves for Spectral curve of
Cortes et al., 2016

31. Source: Environmental Health
Mapping of Mississippi River delta most probable areas of
aphid attack in wheat

32. Aerial photograph through Abd El-Ghany et al., 2020

36. Developed by: Department of
Agriculture & Cooperation
Available in: English & 6 other
Indian languages
Year of Development: 2016
Platform:
https://play.google.com/store/apps/det
ails?id=in.cdac.bharatd.agriapp
Android Beneficiaries: Farmers &
others
Function
It provides information on five critical
parameters- weather, input dealers,
market price, plant protection and
expert advisories.

37. Developed by: ICAR-IIHR
Year of Development: 2017
Platform:
https://play.google.com/store/ap
ps/details?id=com.mangoapp55
&hl=en_IN&gl=US
Language: English & Kannada
Function:
1. The crop protection aspects
comprises of various
diseases affecting mango
crops, viz., anthracnose,
blossom blight, leaf blight,
powdery mildew, dieback,
etc., and
2. the pest management
modules comprises of
infestation of fruit fly ,
mango hopper, stone weevil,
mealy bug, shoot borer, stem
borer, etc.
Developed by: UAS B, GKVK
Year: 2016
Language: English & Kannada
Platform: Android
https://play.google.com/store/apps/details?id
=com.kfs.mango&hl=en_IN&gl=US

38. Year: 2021
Developed by: IASRI
URL:
https://play.google.co
m/store/apps/details?id
=net.iasri.kisaan2.o
Android Beneficiary:
Farmers
Available in English &
12 Indian languages
Benefits
This app integrates
mare than 300
Agricultural related
apps developed by
ICAR Institutes in an
aggregator android
mobile app.

39. Developed by: National
Informatics Centre
Year: 2016
Platform: Android
https://play.google.com/store
/apps/details?id=igkv.igkvcr
opdoctor&hl=en_IN&gl=US
Language: English & Hindi
Beneficiary: Farmers
Function
It disseminates disease,
insect, nutrient deficiency of
crop information to the
farmers as required.
Developed by: PEAT GmBH
Year: 2015
Platform: Android
https://play.google.com/store/apps/details?id
=com.peat.GartenBank
Available in 18 Languages
Function
To diagnose pest damages, plant diseases and
nutrient deficiencies, offers corresponding
treatments (Weather forecast also)

40. Artificial intelligence based pest & diseases diagnostic tools
1. PLANTIX
2. AGRIO
3. FARMWAVE
4. PLANT VILLAGE
5. RICE DOCTOR
6. AGRO SMART
7. KOPPERT IPM

42. Year: 2017, last updated on 12/3/
2023
Website is designed, developed & maintained
by ICAR National Fellow Project under Dr.
GK Mahapatro
URL: https://www.termitexpert.in
It provides introduction on termites,
its management including the
innovative approaches & ITKs. Also
gives information on recent news,
publications and extension works on
termites.
Eclinik option helps to get expert
advice on termite damage by
uploading the pics.
And the same is available as mobile
app.

43. Year of Development:18th April, 2022
Launched by: National Informatics Centre
Beneficiaries: Farmers, Exporters, Importers and Industrialists
Language: English
URL of the platform: https://cropuser.cgg.gov.in/#/
Aim: is to improve the service delivery through integrated IT
Solution for Comprehensive Registration of Pesticides
Objectives:
1. To develop a real-time, user friendly IT Solution without any
manual intervention
2. To integrate with all the stakeholders in the system for
efficiency
3. To develop Dashboard at various levels of hierarchies for
speedy delivery of services
4. MIS Reports based on the day to day requirements of the
Ministry, Department, CIBRC, and other stakeholders of the
system

44. Year of Development:18th April, 2022
Launched by: National Informatics Centre
Beneficiaries: Farmers, Exporters,
Importers and Industrialists
Language: English
URL of the platform:
https://pqms.cgg.gov.in/pqms-angular/home
Mandate:
To prevent the entry, establishment and
spread of exotic pests in India as per DIPA
act, 1914.
Function:
 PQMS facilitates Importers to apply
online for Import Permit, Import Release
Order and Exporters to apply online for
Phyto-sanitary Certificate.

45. Year of Development: 2015, but
modified in 2021
Launched by: National Informatics
Centre
Beneficiaries: Farmers
Language: English
URL of the platform:
https://farmer.gov.in/
Function:
Farmer’s Portal is a one stop shop for
farmers where a farmer can get
information on a range of topics
including seeds, fertilizer, pesticides,
credit, good practices, dealer network,
and availability of inputs, beneficiary list
and agromet advisories.

46. Year of Development: 2013
Beneficiaries: Farmers
Language: 12 languages
URL of the platform:
https://mkisan.gov.in/
Function:
 mKisan SMS Portal enables all
Central and State government
organizations in agriculture and
allied sectors to give information
or services or advisories to
farmers by SMS in their
language, preference of
agricultural practices and
location.
(51969 or 7738299899)

47. Year: 2000
Beneficiaries: Farmers
Language: English
URL: http://agri.and.nic.in/Default.htm
Function of the biocontrol programme:
1.Standardization of methods of mass production
of predators, parasitoids & pathogens.
2. Utilization and evaluation of predators,
parasitoids and pathogens in different agro-
ecosystem.
3. Training to the trainers & farmers in
identification, production, utilization.
Beneficiaries: Farmers & Industrialists
Language: English & Hindi
URL: https://niphm.gov.in/general/objective.htm
Mission:
1. It is to assist the States and the Government of India in
increasing the efficiency of the existing pest and disease
surveillance and control system, certification and
accreditation systems through a core role as a training and
adaptive research centre in the field of extension and policy
developments related to plant protection.

48. Some other important web portals regarding plant protection are
1. Central Integrated Pest Management Centre https://www.cipmcjk.nic.in/
2. ICAR- National Rice Research Institute https://icar-nrri.in/crop-protection/
3. Central Plantation Crops Research Institute https://cpcri.icar.gov.in/
4. Directorate of Plant Protection Quarantine & Storage https://ppqs.gov.in/

49. Social Media
It is the means of interactions among people in which they create, share, and/or exchange
information and ideas in virtual communities and networks by producing, storing, retrieving, and
transferring material in any form i.e., text, photos, video etc (Suchiradipta and Saravanan, 2016).
Level of Digital Penetration in World and India (Ayush and Abhilash, 2022)

50. Classification of Social Media
• Collaborative projects
• 2. Blogs & Microblogs
• 3. Content Communities
• 4. Social Networking Sites
• 5. Virtual social games
.
• 6. Virtual social worlds
Kaplan and Heinlein, 2010

51. Social Media Users in World and India (GOI, 2022)

52. Social
Media
Peer
Networking
Visual
Identification
Live
Consultation
&
Educational
Content
Marketplace
Insights
Crowd
sourcing
Solutions
Customer
Feedback
Information
Sharing
Applications
of Social
Media in
Crop
protection
Mukherjee, 2017

53.  In New Zealand, the UK, the US, Australia, discussions are facilitated between farmers
and agribusinesses under the AgChat model, which is a Twitter online discussion group.
 Maharashtra government in 2021 started promoting policies and schemes related to
agriculture through WhatsApp.
 The central government’s Pradhan Mantri Gramin Digital Saksharta Abhiyan under
Digital India launched in July, 2015 that aims to increase digital literacy in the country
can facilitate rural farmers to get benefitted from social media.
 Krishi Jagran: It is a popular agricultural magazine in India that uses social media
platforms like Facebook and YouTube.
 E-commerce platforms: like AgroStar and BigHaat use social media to connect farmers
with products for plant protection, such as pesticides, herbicides, and fungicides.
Source: https://agriculturepost.com/opinion/social-media-empowering-farmers-to-take-judicious-actions/

54. Facebook (IIHR) ICAR official Instagram Youtube (PUSA Samachar)

55. Linked In X (Twitter) Whatsapp

56. Limited Digital Infrastructure (rural) Access to Technology (remote areas)
Digital Literacy Cost of Technology
Data Privacy & Security
Challenges/Limitations for Digital
Transformation
Language & Region Diversity Resistance to change
Policy & Regulation Scalability
Training & Support

57. 1. Blockchain technology is a decentralized and distributed
ledger system that records transactions across multiple
computers in a way that ensures transparency, security, and
immutability.
 Traceability
 Data Security
 Smart Contracts
2. Robotics is a domain in artificial intelligence that deals with
the study of creating intelligent and efficient robots.
A. These are capable of performing tasks in the physical
world, often in situations that are hazardous, dull, or
impractical for humans.
B. Robots equipped with sensors, cameras, and spraying
equipment.
C. Through the use of machine learning algorithms, these
robots are trained to accurately identify and target pests,
and diseases, optimizing the effectiveness of crop
protection strategies.
Future Scope

58. Intelligent autonomous robot vehicle
camera sensor (automatic focusing,
resolution 1600×1200 and camera model

59. Mechanical arm movement based on
recognition similar probability
(a) - (d) Motion state of the arms;
(e)-(f) The slide rails for the horizontal and vertical
movement of the arms
The original images and the obtained binary
probability images after inverse

60. The recognition results and the robot arm action

61. A sample of five classes of insect pest images are
collected from public dataset

62. Classification results of the
developed mobile application
User Interfaces of developed
mobile app for recognizing crop

63. Performance evaluation & comparative accuracy of agricultural pest classifiers at three different sizes of

64. Conclusion
Digital Transformation makes a way for interaction between farmers, scientists,
government has many indirect advantages: data (Scientists), policy making
(Govt.), yield advantage (Farmers).
Increased
Efficiency
Data Driven
Decision
Making
Automation
& Predictive
Analytics
Monitoring
Knowledge
Sharing &
Innovation

65. Thank you VERY MUCH…