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3. A Project Presentation
On
"PREDICTION OF PLANT LEAF DISEASES USING DRONE AND
IMAGE PROCESSING TECHNIQUES“
Presented by:
Mr. Mate Abhishek P.
Mr. Shinnde Mayur K.
Mr. Kadam Krishna R.
Miss. Sonawane Pooja K.
TB Computer Engineering,
SND College of Engineering and Research Center, Babhulgaon, Yeola , Dist Nashik
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Under The Guidance of:
Prof. Ramesh Daund.

4. Outline of Presentation
 Abstract
 Introduction
 Existing System
 Proposed System
 Advantages Of Proposed System
 System Architecture
 Software Requirement
 Hardware Requirement
 Conclusion
 References
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5. Abstract
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 The automatic detection of plant leaf diseases are highly preferred in the field of agricultural
information. Deep learning is a hot research topic in pattern recognition and machine learning at
present, it can successfully solve these problems in vegetable pathology. In this study, we propose
a new leaf diseases detection method based on convolutional neural networks (CNNs) techniques.
Using a dataset of 260 natural images of diseased and healthy leaves captured from experimental
field. To improve the detection accuracy of leaf diseases and reduce the number of network
parameters, the CNN model based on deep learning is proposed for leaf disease detection.

6. Introduction
This project presents deep convolutional networks model to achieve fast and
accurate automated detection by using different plant leaf disease images .plant leaf
diseases have various symptoms. It may be more difficult for inexperienced farmers
to detect diseases than for professional plant pathologists. As a verification system in
disease detection, an automatic system that is designed to identify crop diseases by
the crop’s appearance and visual symptoms could be of great help to farmers.
Many efforts have been applied to the quick and accurate detection of leaf diseases.
By using digital image processing techniques and neural networks, we can detect
plant leaf disease .Deep learning has made tremendous advances in the past few
years. It is now able to extract useful feature representations from a large number of
input images. Deep learning provides an opportunity for detectors to identify crop
diseases in a timely and accurate manner, which will not only improve the accuracy
of plant protection but also expand the scope of computer vision in the field of
precision agriculture.
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7.  Plant diseases are generally caused by pest, insects, pathogens and decrease the
productivity to large scale if not controlled within time. Agriculturists are facing lose due
to various crop diseases. The proposed system provides the solution for regularly
monitoring the cultivated area and provides the automated plant leaf disease detection.
The objective of the proposed system is to early detection of plant diseases as soon as it
starts spreading on the outer layer of the leaves.
Problem Statement
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8. Existing System
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 Plant identification is not exclusively the job of botanists and plant ecologists. It
is required or useful for large parts of society, from professionals (such as
landscape architects, foresters, farmers, conservationists, and biologists) to the
general public (like ecotourists, hikers, and nature lovers). But the identification
of plants by conventional means is difficult, time consuming, and (due to the use
of specific botanical terms) frustrating for novices. This creates a hard-to-
overcome hurdle for novices interested in acquiring species knowledge. In recent
years, computer science research, especially image processing and pattern
recognition techniques, have been introduced into plant taxonomy to eventually
make up for the deficiency in people's identification abilities.

9. Proposed System
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 We propose an end-to-end trainable system for plant leaf disease detection. In
contrast to the existing deep neural network-based methods which directly estimate
the latent clean image, the network use filter to remove noise.
 First, the leaf samples were collected and images were acquired. The leaf images
were then pre-processed and fed into the feature extraction step .Lastly, the extracted
features were trained and classified by using convolutional neural network algorithm.
And finally it detects plant leaf disease.

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Literature Survey
Paper name Authors Year of publication Algorithm
Hierarchical Learning of
Tree Classifiers for Large-
Scale Plant Species
Identification.
Jianping Fan, Ning
Zhou, Jinye Peng,
Ling Gao.
2015
hierarchical multi-task
structural
learning algorithm.
An Individual Grape Leaf
Disease Identification
Using Leaf Skeletons and
KNN Classification
N.Krithika,
dr.A.Grace selvarani. 2017
Tangential
Direction (TD) based
segmentation algorithm
Plant Disease Detection
Using Leaf Pattern: A
Review
Vishnu S, A. Ranjith
Ram 2015
K-means clustering
technique.
Classification of Cotton
Leaf Spot Diseases Using
Image Processing Edge
Detection Techniques
P.Revathi,
M.Hemalatha 2012
HPCCDD algorithm

11. Advantages of Proposed System
 The deep learning network have to do classification network learns
discriminative features of the extracted from images.
 This model is easily detect plant leaf disease.
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12. System Architecture
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13. System Requirement
HARDWARE REQUIREMENTS:
Processor : Pentium –IV
Speed : 1.1 Ghz
RAM : 8 GB
Hard Disk : 40 GB
Key Board : Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : LCD/LED
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14. Software Requirements:
• Operating system : Window 10
• Coding Language : Python
• Design constraints : Spyder
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15. Flow of the System
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16. Conclusion
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 This project provides very accurate deep learning solution for detecting plant
leaf disease which makes use of convolutional neural network for classification
purpose. The presented model used the dataset that consists of number of images
for training the model .
 As we increase the number of images the accuracy of the model is also
increased.after training the model it will able to detect plant leaf disease from
new input images.

17. References
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 An Individual Grape Leaf Disease Identification Using Leaf Skeletons and KNN Classification
(2017) N.KRITHIKA, DR.A.GRACE SELVARANI.
 Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.(2015)
Jianping Fan, Ning Zhou, Jinye Peng, Ling Gao.
 Plant Disease Detection Using Leaf Pattern: A Review(2015) Vishnu S, A. Ranjith Ram.
 Detection of Diseases on Cotton Leaves Using K Mean Clustering Method(2015) Pawan P.
Warne, Dr. S. R. Ganorkar.

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Mate Abhishek Shinde Mayur Kadam Krishna Sonawane Pooja
THANK YOU