Leaf Disease Detection at Different Stages with Percentage estimation and Suggesting Remedial Measures using Convolutional Neural Network

1. TITAL
LEAF DISEASE DETECTION AT DIFFERENT STAGES WITH
PERCENTAGE ESTIMATION AND SUGGESTING REMEDIAL
MEASURES USING CONVOLUTIONAL NEURAL NETWORK.

2. TABLE OF CONTENTS
.
1. Abstract
2.Introduction
3 Objectives
4. Literature
Review
5. Research
Methodology
6. Expected Results
7. Significance
8. Timeline
9. Conclusion
10. References

3. ABSTRACT
• The key elements in plant production and the mitigation of crop yield losses are
the identification and classification of plant diseases. This study suggests a method
for classifying and detecting plant leaf diseases on plants using image processing.
Three fundamental steps make up the described algorithm: Image processing in
advance and analysis identifying plant diseases. Because the plant illness is minute
in form, a person's ability to diagnose it is limited. Computer visualisation
techniques are used in the identification of plant diseases since the process of
monitoring plants is optical in nature. The objective is to accurately identify
disease symptoms that have appeared in leaves.

4. INTRODUCTION
Agricultural crops are susceptible to various leaf diseases caused by fungi,
bacteria, viruses, and other pathogens. These diseases often manifest at different
stages of plant growth, and their severity can vary significantly. Early detection
and accurate estimation of disease severity can help farmers take prompt action
to mitigate the spread of diseases, reduce crop losses, and minimize the use of
chemical pesticides. Traditional methods of disease detection and severity
estimation rely on visual inspection by trained experts, which can be time-
consuming, subjective, and prone to errors. Therefore, there is a need for
automated systems that can accurately identify leaf diseases, classify their stages,
estimate the percentage of leaf damage, and provide appropriate remedial
measures.

5. OBJECTIVES
Develop a diverse dataset of leaf images representing various stages and severities of diseases,
including annotations for disease type, stage, severity percentage, and corresponding remedial
measures.
Design and train a CNN model for accurate leaf disease detection and percentage estimation.
Develop an algorithm to suggest appropriate remedial measures based on the detected diseases.
Implement an algorithm for percentage estimation of disease severity based on the CNN
predictions
Evaluate the performance of the proposed model in terms of disease detection accuracy, severity
estimation, and remedial measure recommendations.
Compare the proposed approach with existing methods to demonstrate its effectiveness.
A.
B.
D.
E.
F.
C.

6. LITERATURE REVIEW
In recent years, the application of Convolutional Neural Networks (CNNs) in leaf disease detection has
gained significant attention due to their ability to extract high-level features from images and achieve
remarkable accuracy. This literature review explores the existing research related to leaf disease
detection, stage classification, percentage estimation, and remedial measures using CNNs.
Numerous studies have utilized CNNs for leaf disease detection, demonstrating their effectiveness in
accurately identifying diseases. For instance, "Using Deep Learning for Image-Based Plant Disease
Detection "Mohanty et al. (2016) developed a CNN-based model called "PlantVillage" that achieved
high accuracy in identifying 14 different crop diseases.
"Leaf Disease Detection and Remedy Suggestion Using Convolutional Neural Networks" by R Vijaya
Saraswathi et al.(2021) has developed a model using CNN trained with python that identifies diseases
and gives remedial measures on three crops.

7.  GAPS IN PREVIOUS RESEARCH
In both the researches the identification of diseases and suggestion of remedial measures hav
e been implemented succesfully but detection of diseases at different stages and percentage
estimation of leaf defected is not discussed and detection is done only on few crops only
HOW THE RESEARCH PROPOSAL FILLS GAP?
This research proposal aims to develope a model that identifies diseases of as many crops as
possible with diseases at different stages along with percentage estimation.

8. RESEARCH METHODOLOGY
Collect a comprehensive dataset of leaf images, including healthy leaves and leaves infected with
various diseases, at different stages and severities.
Data can be collected from different sources:
Online repositories
Research institutions
Local agricultural communities
Plant nurseries and gardens
A. Data Collection:

9. B Data Preprocessing:
C. Image Segmentation: D. Feature Extraction: E. Model Training:
. Perform
preprocessing steps
such as resizing,
normalization, and
augmentation to
enhance the quality
and diversity of the
dataset
Employ image
segmentation techniques to
identify and isolate the
regions of interest (the
affected parts) within the
leaf images. This can be
achieved through various
methods such as
thresholding,edge
detection,or advanced
deep learning-based
approaches like U-Net.
Extract relevant
features from the
segmented leaf regions,
such as texture
patterns, color
histograms, or shape
descriptors. These
features will be used to
train the disease
classification model.
Train a CNN model using the
labeled and augmented dataset.
CNNs are particularly effective for
image classification tasks. The
model will learn to distinguish
between healthy and diseased leaves
and estimate the percentage of leaf
affected by disease for the diseased
samples. Utilize transfer learning
techniques to leverage pre-trained
models on large-scale image datasets
for improved generalization and
convergence speed.

10. WHAT IS ARTIFICIAL
INTELLIGENCE?
• Machine Learning
• Deep Learning
• Neural Network
• Artificial Neural Network
• Convolutional Neural Network

12. Develop an algorithm to estimate the severity percentage of leaf diseases based on the CNN
predictions. This may involve analyzing the affected area or employing regression techniques to
quantify severity accurately.
Develop an algorithm that suggests appropriate remedial measures based on the detected diseases.
This can be achieved by correlating the disease type, stage, and severity with a database of known
remedial measures.
G. Percentage Estimation and Remedial Measure Suggestion:

13. ETHICAL CONSIDERATIONS:
• Informed Consent: Ensure that the participants, such as farmers or data contributors, provide informed
consent for the collection and use of their data.
• Data Bias and Fairness: Address potential biases in the dataset and model training process. Ensure that
the dataset is diverse, representative, and does not disproportionately favor certain groups or regions.
• Transparency and Explainability: Strive for transparency in the research process and the functioning
of the CNN model. Clearly document the methodology, training process, and decision-making
mechanisms of the model.
• Stakeholder Engagement: Involve relevant stakeholders, such as farmers, agricultural experts, and
policymakers, throughout the research process. Seek their input, address their concerns, and incorporate
their feedback to ensure that the research aligns with their needs, values, and goals.

14. EXPECTED RESULTS
A. A trained CNN model capable of accurately detecting leaf diseases at different stages,
estimating their severity percentage, and suggesting appropriate remedial measures.
B. An algorithm for percentage estimation of disease severity based on the CNN predictions.
C. An algorithm for recommending appropriate remedial measures based on the detected
diseases.
D. Comparative analysis demonstrating the superiority of the proposed approach over existing
methods.

15. SIGNIFICANCE
• Automation: The proposed research aims to automate the detection process of leaf diseases,
reducing the need for manual inspection and enabling rapid identification of diseases at different
stages.
• Accuracy and Speed: By utilizing CNNs, the research ensures accurate and efficient detection,
enabling early intervention and preventing significant crop damage.
• Crop Yield Improvement: Identifying diseases at different stages and suggesting appropriate
remedial measures will help farmers adopt timely actions, leading to improved crop yield and
economic benefits.
• Knowledge Contribution: The research will contribute to the field of agricultural technology by
providing insights into the application of deep learning techniques for disease detection and
proposing effective remedial measures.

16. TIMELINE

17. CONCLUSION
This research proposal aims to address the critical challenge of leaf disease detection by
developing a comprehensive CNN-based system capable of identifying diseases at different
stages, estimating their severity percentage, and suggesting appropriate remedial measures.
The outcomes of this research will contribute to improving agricultural practices, reducing crop
losses, and promoting sustainable farming methods.

18. REFERENCES
• "Leaf Disease Detection and Remedy Suggestion Using Convolutional Neural
Networks" by R Vijaya Saraswathi et al.(2021)
• instance,"Using Deep Learning for Image-Based Plant Disease Detection
"Mohanty et al. (2016)