Paddy Crop Disease Detection using Ann, CNN & Resnet101.docx

1. PADDY CROP DISEASE DETECTION USING ANN, CNN
ABSTRACT
Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning
and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely
used in image and video processing, voice processing, and natural language processing. At the same time, it
has also become a research hotspot in the field of agricultural plant protection, such as plant disease
recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can
avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature
extraction more objective, and improve the research efficiency and technology transformation speed. This
review provides the research progress of deep learning technology in the field of crop leaf disease
identification in recent years. In this paper, we present the current trends and challenges for the detection of
plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a
valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time,
we also discussed some of the current challenges and problems that need to be resolved
I. INTRODUCTION
The occurrence of plant diseases has a negative
impact on agricultural production. If plant diseases
are not discovered in time, food insecurity will
increase [1]. Early detection is the basis for
effective prevention and control of plant diseases,
and they play a vital role in the management and
decision- making of agricultural production. In
recent years, plant dis- ease identification has been
a crucial issue. Disease-infected plants usually
show obvious marks or lesions on leaves, stems,
flowers, or fruits. Generally, each disease or pest
condition presents a unique visible pattern that can
be used to uniquely diagnose abnormalities.
Usually, the leaves of plants are the primary
source for identifying plant diseases, and most of
the symptoms of diseases may begin to appear on
the leaves
II. LITERATURE SURVEY
A recognition method for cucumber diseases
using leaf symptom images based on deep
convolutional neural network
AUTHOR:
J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong, and
Z. Sun
ABSTRACT:
Manual approaches to recognize cucumber
diseases are often time-consuming, laborious and
subjective. A deep convolutional neural network
(DCNN) was proposed to conduct symptom-wise
recognition of four cucumber diseases, i.e.,
anthracnose, downy mildew, powdery mildew, and
target leaf spots. The symptom images were
segmented from cucumber leaf images captured
under field conditions. In order to decrease the
chance of overfitting, data augmentation methods
were utilized to enlarge the datasets formed by the
segmented symptom images. With the augmented

2. datasets containing 14,208 symptom images, the
DCNN achieved good recognition results, with an
accuracy of 93.4%. In order to compare the results
of the DCNN, comparative experiments were
conducted using conventional classifiers (Random
Forest and Support Vector Machines), as well as
AlexNet. Results showed that the DCNN was a
robust tool for recognizing the cucumber diseases
in field conditions.
TITTLE :
Basic study of automated diagnosis of viral
plant diseases using convolutional neural
networks
AUTHORS :
Y. Kawasaki, H. Uga, S. Kagiwada, and H.
Iyatomi
ABSTRACT:
Detecting plant diseases is usually difficult without
an experts’ knowledge. Therefore, fast and
accurate automated diagnostic methods are highly
desired in agricultural fields. Several studies on
automated plant disease diagnosis have been
conducted using machine learning methods.
However, with these methods, it can be difficult to
detect regions of interest, (ROIs) and to design and
implement efficient parameters. In this study, we
present a novel plant disease detection system
based on convolutional neural networks (CNN).
Using only training images, CNN can
automatically acquire the requisite features for
classification, and achieve high classification
performance. We used a total of 800 cucumber
leaf images to train CNN using our innovative
techniques. Under the 4-fold cross-validation
strategy, the proposed CNN-based system (which
also extends the training dataset by generating
additional images) achieves an average accuracy
of 94.9 % in classifying cucumbers into two
typical disease classes and a non-diseased class
III. PROBLEM STATEMENT
The general process of using traditional image
recognition processing technology to identify plant
diseases is shown in Fig. 1. Dubey and Jalal [3]
used the K-means clustering method to segment
the lesions regions, and combined the global color
histogram (GCH) color coherence vector (CCV)
local binary pattern (LBP), and completed local
binary pat- tern (CLBP) was used to extract the
color and texture features of apple spots, and three
kinds of apple diseases were detected and
identified based on improved support vector
machine (SVM), and the classification accuracy
reached 93%.
DISADVANTAGES OF EXISTING SYSTEM
Less accuracy, low Efficiency
IV. PROPOSED SYSTEM
In this project we are implementing 3 different
deep learning algorithms such as ANN, CNN and
ResNet101 to predict paddy diseases such as
Healthy, Leaf Black, Brown Spot or Hispa.
To implement this project we have downloaded
Paddy Doctor dataset from KAGGLE which
consists of more than 5000 images and below
screen showing dataset details
ADVANTAGES OF PROPOSED SYSTEM :

3. High accuracy, High efficiency
V. SYSTEM ARCHITECTURE
VI. RESULT ANALYSIS
To run project double click on ‘run.bat’ file to get
below screen
In above screen click on ‘Upload Paddy Disease
Dataset’ button to upload dataset images and get
below output
In above screen selecting and uploading entire
dataset folder and then click on ‘Dataset’ button to
load dataset and get below output
In above screen dataset loaded and in graph x-axis
represents paddy disease and y-axis represents
number of images found for that disease in dataset
and now close above image and then click on
‘Preprocess Dataset’ button to process images and
get below output
In above screen we can see dataset contains 6061
images and application using 80% (4848) images
for training and 20% (1213) images for testing and
we can see sample processed image also and now
close that image and dataset is ready and now click
on ‘Run ANN Algorithm’ button to train ANN and
get below output

4. In above screen with ANN we got 67% accuracy
and we can see other metrics also like precision,
recall and FSCORE and in confusion matrix graph
x-axis represents Predicted Labels and Y-axis
represents True Labels and all boxes in diagnol
represents correct prediction count and remaining
boxes contains incorrect prediction count. In above
graph we can see there so many boxes with
incorrect prediction count so ANN is not accurate
and now close above graph and then click on ‘Run
CNN Algorithm’ button to get below graph
In above screen with CNN we got 93% accuracy
and in confusion matrix graph different colour
boxes in diagnol contains correct prediction count
and same blue colour boxes contains incorrect
prediction count. In above graph we can see only
few incorrect counts in blue boxes so we can say
CNN is little good in performance. Now close
above graph and then click on ‘Run Resnet
Algorithm’ button to get below output
In above screen with Resnet we got 98% accuracy
and in blue boxes only 2 or 3 images are
incorrectly predicted and in diagonal different
colour boxes we can see more number of images
are correctly predicted. Now click on ‘Comparison
Graph’ button to get below graph
In above graph x-axis represents algorithm names
and y-axis represents accuracy and other metrics in
different colour bars and in all algorithms
RESNET got high accuracy and now close above
graph and then click on ‘Predict Disease from Test
Image’ button to upload test image and get
prediction output

5. In above screen selecting and uploading test image
and then click on ‘Open’ button to get below
output
In above screen image is predicted as Healthy and
similarly you can upload and test other images
Above image is leaf blast
Above image is brown spot
Above image predicted as Hispa and if we upload
other images then will get below output
Above image predicted as Hispa and if we upload
other images then will get below output
VI. CONCLUSION
In this paper, we have introduced the basic
knowledge of deep learning and presented a
comprehensive review of recent research work
done in plant leaf disease recognition using deep
learning. Provided sufficient data is available for
train- ing, deep learning techniques are capable of
recognizing plant leaf diseases with high accuracy.
The importance of collect- ing large datasets with
high variability, data augmentation, transfer
learning, and visualization of CNN activation
maps in improving classification accuracy, and the
importance of small sample plant leaf disease
detection and the importance of hyper-spectral
imaging for early detection of plant disease

6. VII. REFERENCES
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