DISEASE CLASSIFICATION IN
WHEAT FROM IMAGE USING CNN
A Synopsis Submitted
In Partial Fulfillment of the Requirements
for the Degree of
BACHELOR OF TECHNOLOGY
in
Computer Science and Engineering
by
Gyan Prakash Pandey (2102400100036)
Ansh Shukla (2102400100017)
Gaurav Shishodia (2102400100035)
Ansh Tyagi (2102400100018)
Under the Supervision of
Mr. Gaurav Sharma
DR. APJ ABDUL KALAM TECHNICAL
UNIVERSITY
(Formerly Uttar Pradesh Technical University)
LUCKNOW
January , 2025
DECLARATION
We hereby affirm that the research presented in this report, titled “Disease
Classification in Wheat from Image using Convolutional Neural Networks
(CNN),” has been conducted solely by us. We confirm that this work has not been
submitted for the award of any other degree or diploma at any other university or
institution.
We have appropriately acknowledged all external sources and authors for the
words, ideas, diagrams, graphics, computer programs, experiments, and results that
are not our original contributions. Quotation marks have been used where
necessary to identify verbatim content, and due credit has been given to the
original authors and sources.
We declare that this work does not contain any plagiarized material, and the
experiments and results presented in this report have not been altered in any way.
In case of any allegations regarding plagiarism or the manipulation of experiments
and results, we accept full responsibility and accountability.
Name
: Ansh Shukla
Roll No.
2102400100017
Name
: Gyan Prakash Pandey
Roll No.
2102400100036
Name
: Gaurav Shishodia
Roll No. 2102400100035
Name
: Ansh Tyagi
Roll No. 2102400100018
i
CERTIFICATE
Certified
that
Ansh
Pandey(2102400100036),
Shukla(2102400100017),
Gaurav
Gyan
Shishodia(2102400100035),
Prakash
Ansh
Tyagi(2102400100018) has carried out the Project / Research entitled “Disease
Classification in Wheat from Image using Convolutional Neural Networks
(CNN)” for the award of Bachelor of Technology
from Dr. APJ Abdul
Kalam Technical University, Lucknow under our supervision. The project /
research embodies results of original work, and studies are carried out by the
students himself and the contents of the work do not form the basis for the award
of any other degree to the candidate or to anybody else from this or any other
University / Institution.
(Mr. Gaurav Sharma)
(Assistant Professor)
(CSE Department, SDEC Ghaziabad)
Date :
ii
TABLE OF CONTENTS
Declaration
i
Certificate
ii
0.1
Introduction ................................................................................ v
0.1.1
Background ................................................................... v
0.1.2
Problem Identification ................................................. v
0.1.3
Significance of the Problem ........................................ vi
0.1.4
Research / Project Questions and Objectives ................ vi
1 Research Objectives
1.1
1.2
1
Primary Objectives ...................................................................... 1
1.1.1
Objective 1.................................................................... 1
1.1.2
Objective 2.................................................................... 1
Secondary Objectives .................................................................. 1
1.2.1
Objective 3 ................................................................... 1
1.2.2
Objective 4 ................................................................... 1
2 Literature Review
2-3
2.1
Key Concepts and Definitions ................................................. 2
2.2
Historical Perspective .............................................................. 2
2.3
Theoretical Framework ............................................................ 2
2.4
Previous Research ..................................................................... 3
2.5
Current State of the Field ........................................................ 3
2.6
Identified Gaps ........................................................................... 3
iii
3 Research Methodology
4-5
3.1
Research Design .......................................................................... 4
3.2
Data Collection ......................................................................... 4
3.2.1
Primary Data ................................................................ 4
3.2.2
Secondary Data .............................................................. 4
3.3
Data Analysis.............................................................................. 5
3.4
Sampling .................................................................................... 5
3.5
Ethical Considerations ................................................................ 5
4 Data Collection and Analysis
4.1
6-7
Data Collection Methods . . . . . . . . . . . . . . . . . 6
4.1.1
Primary Data Collection . . . . . . . . . . . . . 6
4.1.2
Secondary Data Collection . . . . . . . . . . . . 6
4.2
Data Preprocessing . . . . . . . . . . . . . . . . . . . . 6
4.3
Data Analysis Techniques
. . . . . . . . . . . . . . . .7
4.3.1
Quantitative Analysis . . . . . . . . . . . . . . . 7
4.3.2
Qualitative Analysis . . . . . . . . . . . . . . . 7
References
8
iv
0.1
Introduction
0.1.1 Background
India is considered a country that is rich in culture, varied in
traditions, and full of diverse cuisines. Agriculture forms an
important sector within the economy. Most of the Indian population
relies upon farming as the source of their income. One of the prime
crops cultivated here is wheat; it provides such essential minerals to
the body such as magnesium and selenium, important for good
health. However, wheat plants, especially their leaves, are vulnerable
to leaf rust. Leaf rust is a type of fungal disease that can severely
damage the crop. Mainly, these diseases target the leaves. The use of
modern technologies such as Deep Learning and computer vision in
Convolutional Neural Networks can identify these diseases in their
leaves. This method helps in the early detection of such diseases,
which is important for preventing crop damage and improving yields.
This project applies machine learning techniques to improve wheat
disease prediction from images of the leaves. This can be used with
Convolutional Neural Networks, where diseases like leaf rust will be
detected in a very precise manner, thereby allowing farmers to take
necessary precautions to protect the crops at the earliest possible
stages.
0.1.2 Problem Identification
The present approaches used to identify wheat diseases often employ
straightforward detection techniques that do not take into account regional
variations or unique disease kinds. These might not be suitable for all type
of environment or illness. A machine learning model, more precisely a
Convolutional Neural Network, that uses real-time wheat leaf images,
weather, and environmental factors to predict a more accurate, regionspecific disease type can further increase the likelihood of improved
disease identification precision.
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0.1.3 Significance of the Problem
Knowing how to precisely predict wheat diseases is very significant for
farmers that want to help protect their crop and make most out of all their
farming activities. If any disease is detected too late then it can highly damage
the entire crop, or farmers waste money on unwarranted treatments as well.
Using machine learning, especially Convolutional Neural Networks (CNN),
can help detect diseases more accurately and quickly, making it a more
reliable solution than traditional methods, and ultimately helping farmers
manage their crops better.
0.1.4 Research / Project Questions and Objectives
The main research questions for this project are :
• How can we use machine learning to accurately detect wheat
diseases by analyzing images of wheat leaves, combined with weather
and environmental information?
• What are the key factors, like location, weather, and the condition of
the leaves, that the model needs to consider to predict wheat diseases
effectively?
• Can the system be readily adapted to various types of wheat,
locations, and conditions in order to provide more accurate detection
of diseases across a wide variety of situations?
vi
Chapter 1
Research Objectives
1.1
Primary Objectives
1.1.1
Objective 1
Our objective would be to use machine learning for the
development of a model that would predict diseases on wheat
plants by looking at pictures of the wheat leaves and using factors
such as weather and environmental conditions.
1.1.2
Objective 2
We would like to build an easy-to-use platform where users like
farmers can upload pictures of their wheat leaves along with basic
information such as location and weather. The system will then give
them predictions about possible diseases in their crops.
1.2
Secondary Objectives
1.2.1
Objective 3
We plan to test different machine learning techniques to see which
one works best for predicting wheat diseases. The idea is to compare
how accurate each method is in real-world situations.
1.2.2
Objective 4
We aim to improve the model's predictions by including real-time data
on weather and environmental conditions. This will make the disease
predictions more reliable and up-to-date.
1
Chapter 2
Literature Review
2.1
Key Concepts and Definitions
The main aspects of this research are factors like wheat diseases,
environmental conditions, and the use of machine learning
techniques such as Convolutional Neural Networks (CNN). Wheat
plants are very prone to diseases such as leaf rust, which severely
affects crop yield. The study is trying to understand how
environmental conditions influence the development of diseases in
wheat. Applying the machine learning model, especially CNN, in the
images of wheat leaves could accurately identify diseases affecting
wheat and thus improve disease early detection, disease
management. It is indeed an efficient means to manage health
conditions in the crop.
2.2
Historical Perspective
Predicting wheat diseases had, in the past used traditional methods,
which are at times very limited in their accuracy, especially in
catching early signs of disease like leaf rust. The methods often
worked from simple observations to limited knowledge about
environmental factors that may affect the spread of diseases. It was
thus hard to detect the early stages of factors such as weather
conditions and crop variations. However, as technology continues to
advance and embrace the use of image processing and machine
learning in the diagnosis process, detection of crop diseases is
gradually improving. more accurate machine learning models such as
Convolutional Neural Networks have been successful for wheat
disease identification. These models can analyze large datasets and
images, identifying patterns that were previously hard to detect, thus
offering a more reliable and efficient solution for wheat disease
management..
2
2.3
Theoretical Framework
This project is based on supervised learning, which is a kind of machine
learning where the model learns from the labeled data and makes
predictions based on that. For predicting wheat disease, the model uses
image data of wheat leaves along with environmental variables like
weather and soil conditions. Techniques like regression analysis are used
to understand the relationships between these variables and the presence
of diseases. This aims at developing a predictive model of wheat
diseases given input factors, with improvements in the earlier detection
that consequently allows better management of wheat crops.
3
2.4
Previous Research
Different applications of machine learning in the context of wheat
disease identification have been based on improving detection accuracy
and speed. Ferentinos et al. (2018) had applied CNNs for plant disease
detection and provided evidence that through leaf images of crops, CNN
can be used as effective identification of crops' diseases than traditional
methods at a higher degree of accuracy. Mohanty et al. (2016) used deep
learning techniques to classify plant diseases in crops, especially wheat,
to show significant improvement in detection rates when using CNNs
compared with other models. Liu et al. (2018) also brought out the issue
of incorporating environmental parameters such as temperature and
humidity in the disease predictive models for better performance. This
study shows that machine learning integrated with environmental data
can be used to detect early and accurate wheat diseases, thus enhancing
crop management. The studies done above support our research on the
use of CNNs and environmental data in wheat disease classification.
2.5
Current State of the Field
Most the tools for detection of wheat disease based on either basic
image processing techniques or traditional methods, they are limited
in delivering accurate and timely results. On the other hand, machine
learning models, more specifically CNNs, have presented a
promising trend in plant disease identification. Nevertheless, many
the existing systems find it challenging to integrate real-time data
such as weather conditions or environmental factors into their
systems. These shortcomings reduce the efficiency and adaptability
of current tools, and early detection of diseases is extremely
challenging and not so accurate across different regions. Further
developments are required to improve these models for better realtime disease prediction..
2.6
Identified Gaps
A significant research gap is that there are hardly any studies available
that integrate real-time weather and environmental data in wheat disease
detection models. In fact, availability of such data varies from one
4
region to another, which becomes a challenge while developing
solutions for all regions uniformly. Most of the existing models lack the
ability to detect diseases accurately in wheat across diverse
environmental conditions. Despite all the advancements in machine
learning, these models lack flexibility to adapt to different climates and
wheat varieties. Therefore, improved models that incorporate dynamic
environmental data and are more region-specific, accurate disease
predictions are in demand.
5
Chapter 3
Research Methodology
3.1
Research Design
We will consider a structured machine learning approach wherein the
CNN for the detection of diseases in the wheat plant are taken into
account. Images for training will incorporate pictures of leaves from
wheat as well as their respective environmental elements such as
whether, temperature, and humidity peculiar to the studied region.
Ongoing development would be the creation of the model with
performance being a determinant criterion along with suggestions from
the end users. The system, over time of continued training, would finetune its skills of identifying and classifying the disease in greater detail
and will better support the early detection for wheat farming.
3.2
Data Collection
3.2.1
Primary Data
The primary data will include images of wheat leaves from various fields,
covering different wheat diseases, along with environmental factors like
temperature, humidity, and soil conditions. This combination of visual
and environmental data will be used to train the disease detection model..
3.2.2
Secondary Data
Secondary data will include information on wheat diseases and
environmental factors like temperature, humidity, and rainfall. This
data will be gathered from public sources such as government
databases, agricultural agencies, and online platforms. It will support
the primary data and help improve the accuracy of the disease
detection model.
6
3.3
Data Analysis
This study will eliminate any information that is invalid or irrelevant, thereby
keeping the analysis on the right track without allowing results to be
compromised. Any data found to be inconsistent or misleading will be
recognized and rejected. Normalization of the data will then be done as
standard procedures apply in order for variables to scale consistently. This can
be exemplified through environmental variables such as temperature, humidity,
and soil condition. In the model, important factors like the symptoms of
disease, weather, and indicators of crop health would be given a lot of
emphasis. With these careful selections of high-quality data, the ability of the
model to correctly diagnose wheat diseases would improve significantly.
3.4
Sampling
The model will be developed using data from various regions to ensure its
adaptability across different climates and geographical conditions. This
approach aims to make the model applicable for wheat disease detection
worldwide.
3.5
Ethical Considerations
This project will use confidentiality and safety of data in its users. They
also include anonymous data of the user that uses the prediction.
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Chapter 4
Data Collection and Analysis
4.1
Data Collection Methods
4.1.1
Primary Data Collection
Primary data will be collected as images of the wheat leaves in
various fields and fields that may have both healthy and diseased
crops. Environmental factors such as temperature, humidity, and soil
conditions specific to each region will be collected from field
observations and inputs from the local farmers in addition to the
images.
4.1.2
Secondary Data Collection
Secondary data in the form of meteorological data on temperature,
humidity, and rainfall. Meteorological data are also considered under
secondary data because it can be collected from online platforms or
other databases. Additionally, this would be accompanied by data
from relevant agricultural research organizations that relate to the
historical occurrences of wheat diseases and environmental
conditions.
4.2
Data Preprocessing
Some data preprocessing steps will be taken in order to maintain
good quality data; it includes handling of missing values,
normalization of variables, and transformation for image data.
Following are the steps for the proposed system:
Image Preprocessing: The dataset of images shall first experience a
preliminary clean-up to maintain uniformity
Import Modules: The necessary libraries and modules would be
imported for handling the data effectively.
Load Images: The wheat leaves' collected images shall be loaded
from the dataset.
8
Conversion into Binary: The images are converted into the binary
format and saved as pixel arrays to facilitate easy processing.
Removal of Noise: The images will be free from noise to improve
the quality of data, and the analysis will be carried out more
effectively.
Architecture of the Model: The architecture of the input layer for
the neural network will be described, and the model will be ready to
process data.
Encoding: Encoding layers will be applied to the data to put it in a
proper format to train.
Training of Model: The model is trained on preprocessed data in
order to be able to identify the wheat diseases properly.
Total Images: 20 diseases×1000 images per disease=20,000.
Barley yellow dwarf
Black chaff
Common root rot
Fusarium head blight
Healthy wheat
Leaf rust
Powdery mildew
Tan spot
Wheat loose smut
Wheat soil-borne mosaic
Wheat streak mosaic
Karnal bunt
Septoria leaf blotch
Wheat yellow rust
Wheat black stem rust
Wheat tan spot
Wheat mosaic
Wheat smut
Wheat powdery mildew
Wheat chlorosis
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
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The focus of this system design will be on the areas that affect the performance of the
model. It will start by gathering a massive dataset that is representative of various
wheat diseases. With 1,000 images per disease of 20 different diseases, there will be
20,000 images in total, which ensures that there will be enough data for proper model
training.
Preprocessing will be of utmost importance for improving the efficiency of the model.
This is done by enhancing the images in order to make the input more quality and also
extracting features for reducing the dimensionality without losing the important
information. The model can work effectively by focusing on some features like
texture, color, and shape of the wheat leaves.
The data will be split into training (70%), validation (20%), and testing (10%) subsets
to ensure fair evaluation. Training accuracy, validation accuracy, and testing accuracy
will be used to measure the model’s performance and its ability to generalize to
unseen data.
To ensure that the model generalizes well, we will divide the data into three parts:
70% for training, 20% for validation, and 10% for testing. This allows for a fair
evaluation of the model’s accuracy. Performance will be measured using training
accuracy, validation accuracy, and testing accuracy to ensure the model performs well
on unseen data.
In addition to the initial training, the model will undergo testing with various input
samples to evaluate its real-world performance. Testing accuracy will be a key metric
to assess how the model performs in practical scenarios.
Finally, we will use evaluation metrics such as F1-score, recall, and precision to
assess the model’s ability to accurately identify wheat diseases. These metrics will
help ensure the model minimizes false positives and false negatives, improving its
overall reliability.
By focusing on a high-quality dataset, proper preprocessing, CNN-based
classification, and robust evaluation, this system will be able to efficiently classify
wheat diseases and provide valuable insights for agricultural applications.
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4.3
Data Analysis Techniques
4.3.1
Quantitative Analysis
Mean Absolute Error (MAE) or Root Mean Square Error (RMSE) these
parameters help us in evaluating the effectiveness of the model in
predicting the disease status of wheat crops.
Mean Absolute Error (MAE) is the average of all absolute discrepancies
between the predicted and the actual outcomes. Therefore, the lower the
MAE, the more accurate the forecasts are.
Root Mean Square Error (RMSE) however is the square root of the
mean of the squared differences between the predicted and the observed
values. RMSE is especially important in forecasting because it is usually
the measure of the largest errors.
4.3.2
Qualitative Analysis
It is through gathering feedback from the application users that the
performance of the application can be improved. It helps in knowing the
extent to which the predictions match real observations and the user
experience with the interface, which refines the system. The usability,
prediction clarity, and ease of use areas help to find the improvement
needed. In the engagement of the users, this application is much more
intuitive, reliable, and user-friendly to use, enhancing its overall
effectiveness in disease detection and decision-making support.
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