Artificial intelligence in plant disease detection

Credit Seminar on
Artificial Intelligence In
Plant Disease Detection
GBS Manikanta Chowdary
PhD 1st Year
Department of Plant Pathology
Faculty of Agriculture
Annamalai University
1

Research Advisory Committee
Chairman
Dr. D. John Christopher
The Professor and Head, Dept. of Plant Pathology
Research Supervisor and Convener
Dr. R. Sutha Raja Kumar
Assistant Professor in Plant Pathology
Members
Dr. L. Darwin Christdhas Henry
Associate Professor in Plant Pathology
Dr. J. Sam Ruban
Associate Professor in Horticulture
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Introduction
• India ranks second worldwide in farm outputs. As per
2018, agriculture employed more than 50℅ of the
Indian work force and contributed 17–18% to
country's GDP.
• India ranks first in the world with highest net cropped
area followed by US and China.
• Plant pests and diseases are the major contributors to
biotic stresses that limit realization of yield potential of
crop-plants.
• The increase in human population will require
additional 70% of food by 2050 (Godfray et al., 2010).
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• Protection of crops against plant diseases in particular, have an
obvious role to play in meeting the growing demand for food.
• Losses caused by pathogens, pests and weeds, are altogether
responsible for losses ranging between 20 and 40 % of global
agricultural productivity (Oerke 2006).
• Crop losses due to pests and pathogens have direct, as well as
indirect impact causing both qualitative and quantitative loses;
they have a number of facets, some with short-, and others with
long-term consequences (Zadoks 2015).
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Plant Disease Detection
• Detection - the action or process of identifying the presence of
something concealed.
• Diagnosis - the identification of the nature of an illness or other
problem by examination of the symptoms.
WHAT IS THE NEED FOR PLANT DISEASE
DETECTION ?
• Early and accurate detection and diagnosis of plant diseases are
key factors for reduction of loses and spreading of diseases.
• To assess the effectiveness of application of cultural, physical,
chemical, or biological methods of containing the pathogens
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• To assess pathogen infection in plant materials in breeding
programs.
• To detect and identify new pathogens rapidly to prevent further
spread.
• To Study pathogenesis and gene functions.
• To resolve the components of complex diseases incited by two
or more pathogens
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Common methods for the diagnosis and detection of plant diseases
include
1. Visual plant disease estimation by human raters,
2. Microscopic evaluation of morphology features to identify
pathogens,
3. Molecular diagnostic techniques
4. Serological assays
5. Microbiological diagnostic techniques
(Bock et al. 2010; Nutter 2010).
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• Traditional, visual estimates identify a disease based on
characteristic plant disease symptoms (e.g., lesions, blight,
galls, tumors, cankers, wilts, rots, or damping-off) or
visible signs of a pathogen (e.g., uredinospores of
Pucciniales, mycelium or conidia of Erysiphales).
• Visual estimation is performed by trained experts and has
been the subject of intensive research and investigation.
• Visual estimation has become more accurate and reliable
due to the availability of detailed guidelines and standards
used for assessment training (Bock et al. 2010; Nutter
2010).
9

10
(a) Botrytis cinerea infecting tomato (b) Sclerotinia sclerotiorum infecting
rapeseed (c) Uromyces viciae-fabae infecting bean (d) Blumeria graminis
infecting barley (e) Ustilago maydis infecting maize,
(f) Cladosporium fulvum infecting tomato (g) Colletotrichum higginsianum
infecting mustard spinach and (h) Magnaporthe oryzae infecting rice

Conventional methods of plant disease detection
• Conventional methods of plant disease detection include
molecular and serological methods that could be used for high-
throughput analysis when large numbers of samples need to be
analysed.
• In these methods, the disease causing pathogens such as
bacteria, fungi and viruses are directly detected to provide
accurate identification of the disease/pathogen.
• All of these methods are time consuming and also require
highly sophisticated laboratories and highly skilled people.
• These methods cannot be applied on a large scale and also they
are not cost effective.
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Serological assays
• Serodiagnosis is based on the concept that When any foreign
protein (antigen) such as a virus, fungi, or a bacterial protein is
injected into a mammal (mice, rabbit, horse) or a bird (chicken
or turkey), it induces the animal to produce specific proteins
called antibodies.
• Antibodies then circulate into the blood or serum of the animal.
• These antibodies react specifically with the antigenic
determinant of the antigen which induces its production i.e. they
bind to the portion of the antigen.
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Different methods of serodiagnosis
Enzyme-linked immunosorbent assay (ELISA)
Western blots
Immunostrip assays
Dot-blot immune-binding assays and
Serologically specific electron microscopy (SSEM).
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• ELISA, first employed in the 1970s, is by far the most widely
used immunodiagnostic technique because of its high
throughput potential.
• In this method, the target epitopes (antigens) from the viruses,
bacteria and fungi are made to specifically bind with antibodies
conjugated to an enzyme.
• The detection can be visualized based on colour changes
resulting from the interaction between the substrate and the
immobilized enzyme.
• The performance of ELISA can be improved greatly with the
application of specific monoclonal and recombinant antibodies
which are commercially available (Gorris et al., 1994, López
M.M. et. al, 2001).
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• For plant disease detection, tissue print-ELISA and lateral flow
devices that enable detection have been fabricated for on-site
detection.
• However, the sensitivity for bacteria is relatively low (105–106
CFU/mL).
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Nucleic acid based methods
Some pathogen detection methods are DNA based:
a. Fluorescence in situ hybridization (FISH)
b. PCR variants : nested PCR (nPCR)
c. Multiplex PCR (M-PCR)
d. Real-time PCR (RT-PCR) and
e. DNA fingerprinting).
Others are RNA based:
a. Reverse transcriptase- PCR and
b. Nucleic acid sequencece - based amplification (NASBA).
• Sample preparation for molecular analysis is critical and
requires reproducible and efficient protocols.
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Polymerase Chain Reaction
• Based on the fidelity of DNA hybridization and replication,
PCR was initially used for highly specific detection of diseases
caused by bacteria and viruses (Cai et al., 2014).
• In addition to the basic PCR technology, advanced PCR
methods such as reverse-transcription PCR (RT-PCR) has also
been used for plant pathogen identification due to its high
sensitivity (Ló pez M M et al., 2003).
• Multiplex PCR was proposed to enable simultaneous
detection of different DNA or RNA by running a single
reaction (James D A, 1999; Nassuth A et al., 2000).
• Real-time PCR platforms have also been used for on-site,
rapid diagnosis of plant diseases based on the bacterial, fungal
and viral nucleic acids (Schaad N W et al., 2002).
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• Nested PCR is used to increase the specificity of DNA
amplification. Two sets of primers are used in two successive
reactions.
• In the first PCR, one pair of primers is used to generate DNA
products, which may contain products amplified from non-
target areas.
• The products from the first PCR are then used as template
in a second PCR, using one ('hemi-nesting') or two different
primers whose binding sites are located (nested) within the first
set, thus increasing specificity.
• Reverse transcriptase-PCR is a laboratory technique
combining reverse transcription of RNA into DNA (in this
context called complementary DNA or cDNA ) and
amplification of specific DNA targets using polymerase chain
reaction(PCR).
• It is primarily used to measure the amount of a specific RNA.
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• Fluorescence in-situ Hybridization (FISH)
• It is a type of molecular detection technique, which is applied
for bacterial detection in combination with microscopy and
hybridization of DNA probes and target gene from plant
samples (Kempf, V.A. 2000) .
• Due to the presence of pathogen-specific ribosomal RNA
(rRNA) sequences in plants, recognizing this specific
information by FISH can help detect the pathogen infections in
plants.
• In addition to bacterial pathogens, FISH could also be used to
detect fungi and viruses and other endosymbiotic bacteria that
infect the plant (Hijri, M. 2009, Kilot A, et al, 2014).
• The high affinity and specificity of DNA probes provide high
single-cell sensitivity in FISH, because the probe will bind to
each of the ribosomes in the sample.
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Advanced Methods of Plant Disease Detection
Artificial Intelligence in Plant Disease Detection
What is AI ?
• Artificial intelligence (AI) refers to the simulation of
human intelligence in machines that are programmed to think
like humans and mimic their actions
• Artificial intelligence (AI) is increasingly common in electronic
devices at home or work, in social media, video streaming
services, electronic commerce, and in internet search engines.
Now, AI is rapidly entering the farming scene.
• The steps involved in identification of plant stress by
artificial intelligence are Identification, Classification,
Quantification and Prediction.
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Machine learning (ML)
• With machine learning, computer systems are
programmed to learn from data that is input without being
continually reprogrammed.
• In other words, they continuously improve their
performance on a task—for example, playing a game—
without additional help from a human.
• There are different ways of getting machines to learn.
Some are simple, such as a basic decision tree, and
some are much more complex, involving multiple layers of
artificial neural networks.
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Deep learning (DL)
• Deep learning is the next frontier of machine learning. Just as
machine learning is considered a type of AI, deep learning is
often considered to be a type of machine learning—some call it
a subset.
• While machine learning uses simpler concepts like predictive
models, deep learning uses artificial neural networks designed
to imitate the way humans think and learn.
• With deep learning computer systems, as with machine
learning, the input is still fed into them, but the info is often in
the form of huge data sets because deep learning systems
need a large amount of data to understand it and return
accurate results.
• Then the artificial neural networks ask a series of binary
true/false questions based on the data, involving highly
complex mathematical calculations, and classify that data
based on the answers received.
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Difference between machine learning and
Deep learning
• 1. Human Intervention
With machine learning systems, a human needs to identify
and hand-code the applied features based on the data type
(for example, pixel value, shape, orientation), a deep
learning system tries to learn those features without
additional human intervention.
• 2. Hardware
Due to the amount of data being processed and the
complexity of the mathematical calculations involved in the
algorithms used, deep learning systems require much more
powerful hardware than simpler machine learning systems.
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• 3. Time
As you might expect, due to the huge data sets a deep
learning system requires, and because there are so many
parameters and complicated mathematical formulas
involved, a deep learning system can take a lot of time to
train. Machine learning can take as little time as a few
seconds to a few hours, whereas deep learning can take a
few hours to a few weeks.
• 4. Approach
Algorithms used in machine learning tend to parse data in
parts, then those parts are combined to come up with a
result or solution. Deep learning systems look at an entire
problem or scenario in one fell swoop.
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• 5. Applications
• Basic machine learning applications include predictive
programs (such as for forecasting prices in the stock
market or where and when the next hurricane will hit),
email spam identifiers, and programs that design
evidence-based treatment plans for medical patients.
• Application of deep learning is self-driving cars - the
programs use many layers of neural networks to do things
like determine objects to avoid, recognize traffic lights and
know when to speed up or slow down.
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Difference between Machine learning and Deep learning
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Generally used algorithms of machine
and deep learning for agriculture
• 1. Linear Regression
• 2. Logistic Regression
• 3. Decision Trees
• 4. Support Vector Machine (SVM)
• 5. Naïve Bayes
• 6. KNN (K – Nearest Neighbors)
• 7. K-Means
• 8. Random Forests
• 9. Gradient Boosting and Ada Boost
• 10. Convolutional Neural Networks
• 11. Recurrent Neural Networks
• 12. Recursive Neural Networks
(https://www.simplilearn.com/10-algorithms-machine-learning-
engineers-need-to-know-article)
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Scope of AI in agriculture
• Agriculture is seeing rapid adoption of Artificial
Intelligence (AI) and Machine Learning (ML) both in terms
of agricultural products and in-field farming techniques.
• Cognitive computing in particular is all set to become the
most disruptive technology in agriculture services as it
can understand, learn, and respond to different situations
(based on learning) to increase efficiency.
• 1. Growth Driven by IOT (Internet of things)
• Huge volumes of data get generated every day in both
structured and unstructured format. These relate to data
on historical weather pattern, soil reports, new research,
rainfall, pest infestation, images from Drones and
cameras and so on.
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• Proximity Sensing and Remote Sensing are two
technologies which are primarily used for intelligent data
fusion. One use case of this high-resolution data is Soil
Testing. While remote sensing requires sensors to be
built into airborne or satellite systems, proximity sensing
requires sensors in contact with soil or at a very close
range.
• 2. Image based insight generation
• Drone-based images can help in in-depth field analysis,
crop monitoring, scanning of fields and so on.
• Computer vision technology, IOT and drone data can be
combined to ensure rapid actions by farmers.
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• Feeds from drone image data can generate alerts in real time
to accelerate precision farming which aids in the following
• a. Disease detection
• b. Crop readiness identification and
• c. Field management
• 3. Identification of optimal mix of agronomic products
• Based on multiple parameters like soil condition, weather
forecast, type of seeds, and infestation in a certain area and so
on, cognitive solutions make recommendations to farmers on
the best choice of crops and hybrid seeds.
• The recommendation can be further personalized based on the
farm’s requirement, local conditions, and data about successful
farming in the past.
• External factors like marketplace trends, prices or consumer
needs may also be factored into enable farmers take a well-
informed decision.
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• 4. Health monitoring of crops
• Remote sensing techniques along with hyper spectral imaging
and 3d laser scanning are essential to build crop metrics
across thousands of acres. It has the potential to bring in a
revolutionary change in terms of how farmlands are monitored
by farmers both from time and effort perspective.
• This technology will also be used to monitor crops along their
entire lifecycle including report generation in case of
anomalies.
• 5. Automation techniques in irrigation
• In terms of human intensive processes in farming, irrigation is
one such process.
• Machines trained on historical weather pattern, soil quality and
kind of crops to be grown, can automate irrigation and increase
overall yield. With close to 70% of the world’s fresh water being
used in irrigation, automation can help farmers better manage
their water problems.
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Machine Learning based Plant Disease
Detection Systems
• In recent times, server based and mobile based approach
for disease identification has been employed for plant
disease identification.
• Several factors of these technologies being high
resolution camera, high performance processing and
extensive built in accessories are the added advantages
resulting in automatic disease recognition.
• Various researches have taken place under the field of
machine learning and deep learning for plant disease
detection and diagnosis, such traditional machine learning
approach being random forest, artificial neural
network, support vector machine (SVM), fuzzy logic,
K-means method, Convolutional neural networks etc.
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Machine learning in predictions of plant or
pathogen-related molecules
• A complex cascade of defense responses is induced
during plant-pathogen interaction via invading signals
from invaders and/or plants themselves,
• As a result, various defensive reactions, such as
production of reactive oxygen species(ROS),
reinforcement of plant cell wall, and synthesis of defense
enzymes, will be activated and initiated through different
signal transduction pathways (Saunders DG et al., 2012,
Sperschneider et al., 2016).
• Pal et al., 2016 showed that supporting vector machine
(SVM), which was used to predict plant resistance
proteins (R proteins) based on 10,270 features
extracted from amino acids sequences of proteins,
achieved an accuracy of 91.11% on the test datasets.
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• Sperschneider et al.,2015 compared the results of fungal
effector predictions from several machine learning
algorithms, including Naïve Bayes, Naïve Bayes-K,
logistic regression, multilayer perceptron, C4.5 decision
tree and random forest and found out that among the
different machine learning methods, Naïve Bayes
achieved highest performance of prediction.
• Later, Sperschneider et al., 2016 developed a tool
(LOCALIZER) based on SVM to predict effector
subcellular localizations, which provide critical clues
about the functions of effectors in plant cells which
showed accuracy of 91.4% for chloroplast localization,
91.7% for mitochondria localization, and 73% for
nucleus localization
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• Wetterich et al., 2013 detected HLB with SVM based on four
features (uniformity, contrast, correlation and homogeneity)
extracted from fluorescence imaging spectroscopy. By
using this method, the highest classification accuracy was
90% for HLB (Citrus greening) - infected leaves from Brazil.
• Later Wetterich et al. used two machine learning algorithms,
SVM and artificial neural network (ANN), to discriminate HLB
(citrus greening) from zinc-deficiency stress on citrus
leaves. The accuracy was 92.8% for SVM and 92.2% for ANN
respectively.
• Prince G et al.,2015 extracted both local and global statistics
from thermal and visible light image data and used SVM to
identify the powdery mildew-inoculated tomato leaves. They
showed that the machine learning system was able to identify
the tomato leaves infected naturally by powdery mildew.
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• By using deep convolutional neural networks (CNN),
which is the latest generation of machine learning
methods, Sladojevic et al., 2016 has successfully
recognized 13 different plant pathogens and achieved
precision between 91% and 98%.
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Image Processing based detection of
Plant Diseases
• Digital photographic images are important tools in plant
pathology for assessing plant health. Digital cameras are easy
to handle and are a simple source of RGB (red, green, and
blue) digital images for disease detection, identification, and
quantification.
• In recent times, server based and mobile based approach for
disease identification has been employed for disease
identification.
• Tuker and Chakraborty, 2008 had presented software which
detects, characterizes and calculates percentage of diseases
leaf area using digital image processing.
• Di Cui, et al., 2010 had reported research outcomes from
developing image processing methods for quantitatively
detecting soybean rust severity from multi-spectral images.
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40
Calculation of disease severity using diseased pixels
and segmentation

• RGB-colour images with the red, green, and blue channels
have been used to detect biotic stress in plants (Bock et al.,
2010).
• Along with colour information in the RGB, LAB (L for
lightness and A and B for the colour opponent dimensions,
based on nonlinearly compressed coordinates), gray levels,
texture, dispersion, YCBCR, colour space, the spatial
information provides important characteristics of plant
diseases (Bock et al., 2010).
• Several software packages, such as ASSESS 2.0, “Leaf
Doctor,” Scion Image software are available (Pethybridge
and Nelson 2015) which have built in machine learning
algorithms which are used in identification of plant diseases.
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• S. S. Sannakki and V. S. Rajpurohit, proposed a
“Classification of Pomegranate Diseases Based on Back
Propagation Neural Network” which mainly works on the
method of Segment the defected area and color and
texture are used as the features and the categorization is
found to be 97.30% accurate.
• The initial detection of a disease can be based on
machine vision and image processing that will generate
an alert if its symptoms are recognized.
• Symptoms can often be grouped as:
• Underdevelopment of tissues
• Overdevelopment of plant parts like tissues or organs
• Necrosis of plant and
• Alternations like mosaic patterns and altered coloration in
leaves and flowers.
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Machine learning assisted Plant disease detection by RGB Imaging
Host Plant Disease Causal Agent Reference
Cotton
Bacterial blight, Xanthomonas campestris
Camargo and Smith, 2009
Ascochyta blight Ascochyta gossypii
Sugar beet
Sugar beet rust Uromyces betae
Neumann et al., 2014
Ramularia leaf spot Ramularia beticola
Phoma leaf spot Phoma betae
bacterial leaf spot Pseudomonas syringae pv. Aptata
Cercospora leaf spot Cercospora beticola
Citrus Canker Xanthomonas axonopodis Bock et al., 2008
Tobacco Anthracnose Collectotrichum destructivum
Wijekoon et al., 2008Apple Scab Venturia inequalis
Canadian
goldenrod
Rust Coleosporium asterum
43

Machine learning based Multi Spectral
Imaging for Plant Disease Detection
• Spectroscopy is a field aimed at studying how different
materials interact with light, particularly which
wavelengths will be absorbed or reflected by a material
once the material is exposed to rays of light. All the plant
parts such as leaves, stems, fruits etc. also absorb light
and reflect it back. So based on the optical properties of
plant parts, disease detection is possible.
• The optical properties of leaves are characterized by
• Light transmission through a leaf,
• Light that is absorbed by leaf chemicals (e.g., pigments, water,
sugars, lignin, and amino acids), and
• Light reflected from internal leaf structures or directly reflected from
the leaf surface.
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45
Image Source – DJI P4 Multispectral

Reflection pattern in healthy vegetation
• There are three distinguished spectral domains of vegetation
reflectance based on which remote sensing techniques are used.
• In the visible domain (VIS: 0.4-0.7 µm), the main light
absorbing pigments are chlorophyll a and b, carotenoids,
xanthophylls and polyphenols.
• Chlorophyll a displays maximum absorption in the 0.41-0.43
and 0.60-0.69 µm regions, whereas Chlorophyll b shows
maximum absorption in the 0.45-0.47 µm range.
• In the near infrared domain (NIR: 0.7-1.3 µm), absorption is
very low and reflectance and transmittance reach their
maximum values. This is caused by internal scattering at the
air–cell– water interfaces within the leaves.
• In the mid-infrared domain (mid-IR: 1.3-2.5 µm), also called
shortwave-infrared (SWIR), leaf optical properties are mainly
affected by water and other foliar constituents.
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Reflection pattern in sick and dead leaves
• When a plant is under stress, chlorophyll production may
decrease resulting less absorption in blue and red bands in
palisade cells. So along with green band, red and blue bands are also
reflected.
• Hence, yellow or brown color is developed in stressed vegetation.
In stressed or diseased plants, NIR bands are not reflected by the
mesophyll cells, instead they are absorbed by stressed or dead cells.
As a result, dark tones are found in the image.
• Biotrophic fungi such as powdery mildews or rusts have a
relatively low impact on tissue structure and chlorophyll
composition during early infection, perthotrophic pathogens, such
as those that cause leaf spots, often induce degradation of tissue due
to pathogen-specific toxins or enzymes that ultimately results in
necrotic lesions.
• In contrast, powdery mildews and rust fungi produce fungal
structures on the leaf surface that can influence the optical
properties of the plant-pathogen interaction.
49

Reflection pattern in sick and dead leaves
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Normalized difference vegetation index
• Normalized Difference Vegetation Index (NDVI) quantifies
vegetation by measuring the difference between near-infrared
(which vegetation strongly reflects) and red light (which
vegetation absorbs).
• NDVI always ranges from -1 to +1. But there isn’t a distinct
boundary for each type of land cover.
• But when NDVI is close to zero, there isn’t green leaves and it
could even be an urbanized area.
• Overall, NDVI is a standardized way to measure healthy
vegetation. When you have high NDVI values, you have
healthier vegetation.
• NDVI data provides a measurement of crop health.
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Host Plant Disease Causal Agent Reference
Barley
Net blotch Pyrenophora teres
Kuska et al., 2015;
Wahabzada et al., 2014a
Brown rust Puccinia hordei
Powedery mildew Blumeria graminis hordei
Wheat
Head blight Fusarium graminearum
Bauriegel et al. (2011);
Bravo et al. (2003);
Yellow rust Puccinia striiformis f. sp. tritici
Huang et al. (2007);
Moshou et al. (2004)
Sugar beet
Cercospora leaf spot C. beticola
Bergstr¨asser et al.
(2015); Hillnh¨utter et al.
(2011);
Mahlein et al. (2010,
2012, 2013);
Rumpf et al. (2010);
Steddom et al. (2003,
2005)
Sugar beet rust U. betae
Powdery mildew Erysiphe betae
Root rot Rhizoctonia solani
Rhizomania Beet necrotic yellow vein virus
Tomato Late blight Phytophthora infestans Wang et al. (2008)
Apple Apple scab Venturia inequalis Delalieux et al. (2007)
Tulip Tulip breaking virus Tulip breaking virus Polder et al. (2014)
Sugarcane Orange rust Puccinia kuehnii Apan et al. (2004)
55

Deep Learning in the detection of Plant Diseases
• DL is about “deeper” neural networks that provide a
hierarchical representation of the data by means of
various convolutions. This allows larger learning
capabilities and thus higher performance and precision.
• A strong advantage of DL is feature learning, i.e. the
automatic feature extraction from raw data, with features
from higher levels of the hierarchy being formed by the
composition of lower level features (LeCun, Bengio, &
Hinton, 2015).
• DL can solve more complex problems particularly well and
fast, because of more complex models used, which allow
massive parallelization (Pan & Yang, 2010).
• Sources of data used to train the DL model require large
datasets of images containing thousands of images.
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• Some datasets originate from well-known and publicly-
available databases such as PlantVillage, LifeCLEF,
MalayaKew, UC Merced and Flavia, while others
constitute sets of real images collected by the authors for
their research needs.
• The basic deep learning tool used in this work is
Convolutional Neural Networks (CNNs) (LeCun et al.,
1998). CNNs and Support Vector Machines (SVMs)
• CNNs (LeCun et al., 1998) are an evolution of traditional
artificial neural networks, focused mainly on applications
with repeating patterns in different areas of the modeling
space, especially image recognition.
57

• Mohanty et al. (2016) compared two well-known and
established architectures of CNNs in the identification o
26 plant diseases, using an open database of leaves
images of 14 different plants.
• Pawara et al. (2017) compared the performance of some
conventional pattern recognition techniques with that of
CNN models, in plants identification, using three different
databases of (a rather limited number of) images of either
entire plants and fruits, or plant leaves, concluding that
CNNs drastically outperform conventional methods.
• Fuentes et al. (2017) developed CNN models for the
detection of 9 different tomato diseases and pests, with
satisfactory performance.
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Diseases detected by deep learning
Host Disease Pathogen Reference
Apple Scab Venturia inequalis I Lenz et. al., 2015
Apple Cedar rust
Gymnosporangium
juniper virginianae
I Lenz et. al., 2015
Apple Black rot Botrysphaeria obtusa I Lenz et. al., 2015
Banana Black Sigatoka Mycosphaerella fijiensis
L. Zhang et. al.,
2016
Cabbage Blackrot
Xanthomonas
campestris pv.
Campestris
Martinez et.al., 2016
Cassava Brown leaf spot
Cercosporidium
henningsii
Calderon et. al.,
2015
Corn**
Cercospora leaf
spot
Cercospora zeae maydis
Mohanty et. al.,
2016
Corn Common rust Puccinia sorghi Wang et. al., 2016
59

Host Disease Pathogen Reference
Cucumber Downy mildew
Pseudoperonospora
cubensis
P R Rothe et. al., 2015
Peach Bacterial spot Xanthomonas campestris S Yun et. at., 2015
Soybean Downy mildew Peronospora manshurica
Akansha Rastogi et. al.,
2015
Soybean Frog eye leaf spot Cercospora sojina J Schmidhuber, 2015
Tomato Bacterial spot
Xanthomonas campestris
pv. Vesicatoria
Mahlein, 2018
Tomato Late blight Phytopthora infestans Lechenet M et. al., 2017
Tomato** Septoria leaf spot Septoria lycopersici
Lechenet M et. al., 2017
Strawberry Leaf scrorch Diplocarpon earlianum Berthon K et. al, 2018
Tomato Leaf mold Fulvia fulva Heim et. al., 208
Squash Powdery mildew Erysiphe cichoracearum Hijmans R J, 2017
60

Conclusion
• When compared to the conventional methods of plant disease
detection, AI based detection systems are highly efficient, requires
less time, involves less cost and also don’t require trained
professionals.
• The major advantage with AI is that it keeps on learning over the
time. The best example is differentiating between the plant
diseases and nutrient deficiencies, AI can be programmed
accordingly so that it learns the changing pattern over time.
• The only disadvantage is that AI largely relies on the cloud data
platforms, need longer software support and requires fast internet
connectivity, but this can be solved as today the entire world is
being driven by technology and AI is the new buzz word in the
technological industry and the speed of internet was greatly
increased with the introduction of 4G and 5G.
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Future Prospects
• Apart from detecting the plant diseases, AI has a wide range of
application in agriculture.
• AI is useful for the detection of pest damages, weeds, nutrient
deficiencies.
• AI powered robots are being developed for performing farm
operations and also AI is being introduced to farm vehicles.
• AI powered sprayers with AI vision are being developed so that the
spraying becomes more uniform and aids in targeted application of
farm chemicals.
• AI is being used for the prediction of weather parameters to
suggest farmers the appropriate measures with respect to crop
safety.
• There is no wonder to say that in the near future, every aspect of
the farming will be done by the machines powered by AI from
sowing to harvesting.
• Even AI can also aid in marketing of the produce by conducting
deep market analysis based on the time of harvesting and provide
accurate insights.

Thank you for your attention
Any Questions ?
63

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