Enhancement PPT.pptx

Presented by
D N Kiran Pandiri
Reg. No. 19-3-04-118
Supervisor
Dr. R Murugan,
Assistant Professor
Department of Electronics and Communication Engineering
National Institute of Technology, Silchar
Soil and Crop Leaf Disease Classification using
Artificial Intelligence
Proposed Tentative Title

20 May 2023 D.N.KIRAN PANDIRI , Dept. of ECE, NIT Silchar 2
I Welcome the Doctoral Committee to
the Enhancement Seminar
 Chairman : Dr. T Khan, Asst Prof, ECE
 External Examiner : Dr. Sumantra Dutta Roy, Professor, EE, IIT Delhi
 Member : Dr. Chandrajit Choudhury, Asst Prof, ECE
 Nominee of Chairman, Senate : Dr. Asha Rani M A, Asst Prof, EE
 Supervisor : Dr. R Murugan, Asst Prof, ECE

Outline of the presentation
 Broad area of research
 Introduction
 Literature survey
 Problem statements
 Objectives
 Conclusion
 Publications
 Works under communication
 References

Broad area of research
 Image Processing in Agriculture

Introduction
 It is a practice of cultivating
plants and livestock.
 It has been practicing from
many centuries by our
ancestors and by us till now.
 Agriculture with its allied
sectors is the largest source
of livelihood in many
countries across the globe.
Fig.1. Image representation of cultivation and livestocks. [1]

The agriculture and forest lands decreased for the past two decades [2][3].
The global food demand has been increasing with increase in population.
Fig. 2. Agriculture and forest land area declined in statistics from 2000 – 2018 [2]

Crop Loss due to diseases
0
50
100
Yield
Loss
Between 20 - 40 percent of Global crop
yields are reduced each year due to the
damage wrought by plant pests and
diseases.

Usage of pesticides and fertilizers
Fig. 3. Percentage increase in the usage of fertilizers [2]

Decrease in work force
Fig 4. The decline of workforce in the last two decades [2]

Digital Transformation of Agriculture
 There is a need for optimizing the agriculture practices without putting extra
environmental burden.
 The digital transformation of the agriculture sector is to meet the increasing food
demand of the global population explosion without affecting the environmental
conditions and maintaining the profit to the farmers.
 Digitalization of agriculture can reduce the burden of employee workforce, crop
management, soil management and many other.

Motivation
 Digitalization of the agriculture sector that reduces the use of fertilizers and pesticides
and increases the crop yield by maintaining profits to the farmers.
 We have consider two key components which can reduce the usage of fertilizers and
pesticides in increasing the crop yield is
1. Soil type classification
2. Plant leaf disease classification

Soil Type Classification
 Soil is the primary component in agriculture.
 Soil type identification is the key to the success of site-specific crop management.
 Soil management helps in varying the tillage [4].
 Identifying the soil type can reduce or minimize the soil tillage by reducing the
production cost.
 Based on the soil type, the soil conditions like soil texture and moisture content can
be adjusted, which are important for seed and fertilizer placement.
 There is no effective research on physical properties of the soil like soil type, porosity,
texture, structure, density, etc.,

 The conventional methods used for identifying the soil type are
 Laboratory testing [7][8]
 In-situ testing [5][6]
 All these methods are time-consuming, high cost, and require proficiency to analyze
soil type.
 By using digital techniques, researchers used texture, color, particle size, pattern, pH
level, and organic matter as features in distinguishing the different types of soils.
 The soil type is identified based on the texture of the soil sample.
 The soil texture varies on the percentage of sand, silt, and clay present in the soil
sample.

Sample Soil Images
Fig 5. Sample Soil images. (a) Loamy sand, (b)&(d) sand, (c),(e)&(f) clay, and (g) sandy loam.
(a) (b) (c) (d)
(e) (f) (g)

[Ref]
Year
Title Input Data Methods Focus
[9]2015
Soil type classification and mapping using
hyperspectral remote sensing data.
Hyperspectral
image
Gaussian SVM
Classify the soils based on the colour with
hyperspectral data
[10]2019
Compressive spectral imaging system for soil
classification with three-dimensional
convolutional neural network.
3D CNN
Hyperspectral images are used in
classification of soil type. PCA is used in
reducing the feature dimensions.
[11]2016
A smartphone-based soil color sensor: For soil
type classification.
Smart Phone
Camera with
spectral lenses
Linear Discriminant
Analysis
Classified the soil types based on the
Munsel color. The color of the soil
identified by using the spectral
wavelength.
[12]2020
Classification of soil aggregates: A novel approach
based on deep learning.
Stereo pair
images
CNN
Deep Learning methods in classifying
aggregate soil
[13]2012
Soil texture classification algorithm using RGB
characteristics of soil images.
CCD Camera
Images
Linear Regression
Using RGB histogram of the CCD camera
images, classified the soil types
Literature Review on Soil Classification

[Ref]
Year
[14]2018
Soil Characterization and Classification: A
Hybrid Approach of Computer Vision and
Sensor Network
Camera images
and Sensor
data
NN
Features of Soil images were extracted from the images
captured using the DSLR camera and moisture level of the
soils were measured using a sensor. All these data was used
in classification of soil types.
[151999
An artificial neural network for classifying
and predicting soil moisture and temperature
using Levenberg-Marquardt algorithm.
Sensor data
NN with
Levenberg-
Marquardt
Classified two soil classes grass and bare smooth soils based
on the remotely sensed data to measure the moisture of the
soil. Measured the moisture levels at different depths for the
same soil samples and classify the soil types.
[16]2017
Performance of SVM classifier for image
based soil classification.
Android and
SLR Images
SVM
Classified the soil types by extracting the features using LPF,
Gabor filter bank, and color momentum of the soil images
obtained from online mobile images
[17]2020
Soil texture classification using multi class
support vector machine
Texture features of the labelled soil samples were extracted
by using HSV histogram, color moment, Gabor wavelets,
and DWT. The features were classified using SVM
classifier. The soil samples were collected from paddy fields
in Guwahati.
[18]2020
Artificial intelligence system for supporting
soil classification.
CNN
Classified the soils sand, clay and gravel using SLR camera
images

Research Gaps
 Even though many promising methodologies have been developed and implemented,
research on classification of soil type using mobile/SLR camera images is in initial
stages.
 Classification of soil type using mobile/SLR camera images is a challenging task.
 There is no publicly available soil image dataset created by following the standards in
capturing the images of the soil type.
 Classifying the soil type using the texture features of the soil type is complicated but
might give accurate results as the RGB characteristics of the soil vary from region to
region due to climate and environmental conditions.
 Soil classification using images can reduce the computational time and cost in
analyzing the soil type.
 Soil classification will help in soil management in agriculture sector.

Crop Leaf Disease Classification
 The crop management is an important task at every stage of the of the crop growth.
 The crop management involves
Yield Prediction
Crop quality
Leaf disease identification
Weed detection
Crop recognition
 To meet the necessity of the people, the production of the crop has to be increased.
 One of the most important feature in crop management is disease classification.
 Early detection of diseases in plants can reduce the pesticides and fertilizers usage in
protecting and increasing the yield of the crop.

Fig 6. Percentage of primary crops production across the world. [19].

Most investigated crops[29]
Fig 7. Research article count on the primary crop [19]

 Potato (Solanum Tuberosum) has been chosen for plant leaf disease detection.
 Potato stands top in most consuming vegetable across the globe.
 India stands second largest producer nation, with 53.69 million metrics tons produced
during the fiscal year 2021.
 The average consumption of potato by a person is 49.4 pounds during the year 2019
[20].
 Potato leaves are affected due to fungal pathogens is
Early Blight (Alternaria)
Late Blight (Phytophthora)
 The annual potato crop loss due to late blight alone is 6.7 billion dollars across the
world.

Early Blight Late Blight

Literature Review on Potato Leaf Disease
Classification
[Ref]
Year
[21]2018
Using Support Vector Machines classification to
differentiate spectral signatures of potato plants
infected with Potato Virus Y
Hyperspectral
images
SVM
Classification of potato virus Y infected plants
using unmanned aerial vehicle to capture the
hyperspectral images with a spectral range
350-2500 nm in NIR and SWIR wavelengths.
The spatial samples were classified using SVM
[22]2021
An Empirical Study on Machine Learning Models
for Potato Leaf Disease Classification using RGB
Images
Central Potato
Research
Institute
(CPRI), India,
Plantvillage
Gabor filter,
Fine tuned
VGG16,SVM
Collected the real time images from the field
and segmented the region of interest and the
features are extracted using FCNN which are
used in ML for classification using SVM.
[23]2021
Automated abnormal potato plant detection
system using deep learning models and portable
video cameras
Video frame
images
CNN
Abnormal potato plants were identified during
the early growth and mid-growth of the plant
[24]2020
Detection of Potato Disease Using Image
Segmentation and Multi class Support Vector
Machine
Plantvillage
GLCM, Hu,
Color
Histogram, ML
Feature extraction from the diseased
segmented part and classifying the leaves

[Ref]
Year
[25]2021
Ensemble Classification and Feature
Extraction Based Plant Leaf Disease
Recognition
Plantvillage
Dataset
GLCM+LBP+PCA
+LAW texture
mask+LDA+ML
Ensemble methods are created for extracting the features
using different sizes convolution mask and the features
are used in classifying the leaf disease images of Tomato,
Bell Pepper and Potato
[26]2021
Recognition of early blight and late
blight diseases on potato leaves based
on graph cut segmentation
Graph cut + LBP +
ML (SVM)
For Classifying the potato leaf diseases at different
stages, dataset has been segregated and segmented the
images using Graph cut model to detect the diseases. The
features of the images are extracted by using LBP and fed
to ML classifier algorithms.
[27]2021
Automated plant leaf disease detection
and classification using optimal
MobileNet based convolutional neural
networks
Extreme learning +
ML
Classified the tomato plant leaf diseases by optimizing
the hyperparameters of the model using EPO algorithm
[28]2022
Transfer Learning for Multi-Crop Leaf
Disease Image Classification using
Convolutional Neural Network VGG
VGG16
Multi crop leaf disease classification at early stages in
tomato and grape crop leaf. The diseased spot is
annotated by rectangular bounding box.
[29]2020
Identification of Disease in Potato
Leaves Using Convolutional Neural
Network (CNN) Algorithm
CNN
Classification of potato leaf diseases Early Blight and
Late Blight

Research Gap
 Research in identifying the diseased leaves in potato plants is still in early stages.
 Classification is carrying on the laboratory images for developing the models.
 Segmentation of infected leaf in identifying the diseases is a challenging task.
 Availability of real time images is low.
 There is no cost effective infrastructure in monitoring the crop disease leaves.

Problem Statement
1. Creating the standard soil image database.
2. Classifying the standard soil types sand and clay using machine learning algorithm
3. Multi-class soil classification using the soil image database created by considering
the imbalance data.
4. Classification of Phytophthora infestans and Alternaria solani diseases in Potato
plant leaves using deep learning networks.
5. Detection of diseases caused by fungi pathogens in Solanum Tuberosum leaves.

20-05-2023 Department of ECE, NIT Silchar 27
Objective 1
The standard soil image database has been created by capturing the images of the soil sample using
android/SLR camera.
Classifying the standard soil types sand and clay using machine learning algorithm
Objective 2
Multi-Feature Fusion for Soil Image Feature Extraction and Classification using Machine Learning
Multi-class soil classification using the soil image database created by considering the imbalance data
Objective 3
A Smart Soil Image Classification System using Lightweight Convolutional Neural Network
Classification of Phytophthora infestans and Alternaria solani diseases in Potato plant leaves using
deep learning network
Objective 4
POT-Net: Solanum Tuberosum (Potato) Leaves Diseases Classification using an Optimized Deep
Convolutional Neural Network
Detection of diseases caused by fungi pathogens in Solanum Tuberosum leaves
Objective 5
Early Blight and Late Blight disease detection using deep learning models
Creating the standard soil image database

Problem Statement 1
Objective
 The standard soil image database has been created by capturing the images of the soil sample
using android/SLR camera.

Acquiring Soil Samples
 The soil samples are collected from different regions of Andhra Pradesh and Silchar, Assam.
 A total of 96 soil samples are gathered from agricultural fields in these locations.
 The regions include the Godavari river coastal zone, rich in alluvial and sandy soils, drought-
prone areas with sandy and rocky soils, and hill stations suitable for plantation.
 The soil samples were obtained at a depth of 5 to 10 cm below the field surface.
(a) (b) (c)
Fig. 8. Represents the areas where soil samples are collected. (a) Rajahmundry (b) Madanapalle (c) Digged area

Soil analysis in the laboratory
 Each soil sample is subjected to laboratory examination to determine the texture of the
collected soil samples and label the soil type by identifying the fraction of sand, silt, and clay.
 Sieve Analysis
 Hydrometer test
• In sieve analysis, 500gms of sand type soils are taken and
sieved through the different sieve pans of sieve size up to
75μm to find the fraction of sand. If the fraction of silt and
clay that passed through the 75μm sieve pan is greater than 10
percent, we go for a hydrometer test to find the silt and clay
fraction.
Fig.9. Sieve shaker for Sieve Analysis

 For hydrometer test, 33gms of sodium hexametaphosphate and 7gms of sodium bicarbonate
in one liter of water, a solution is made and left for 24 hours.
 Now, 100ml of the solution is mixed with 800ml of water for the hydrometer test.
 In the 900ml prepared solution, 50gm of the tested soil sample is added, and readings are
taken at regular intervals over the following 24 hours.
 The height of the hydrometer from the surface of the solution is recorded at regular intervals.
• The texture of the soil sample is determined
by plotting the percentage of sand, silt and
clay on the United States Department of
Agriculture (USDA) texture triangle.
(a) (b)
Fig. 10. (a) Hydrometer and (b) Hydrometer test

(a) (b)
Fig 11. a) USDA texture triangle for classification. b) USDA texture triangle with the some soil samples plotted.

Experimental Setup
 The images of the acquired soil samples are captured by using a Samsung smartphone
camera.
 The organic components present in the soil are removed, and the moisture content is removed
by drying the soil in a hot air oven at 1000C for 24 hours.
 A chamber with dimensions 45x20x10 cm was constructed with Styrofoam to capture the
images.
• As the box walls are white, allowing the
light that enters the box through the
window will provide the illumination
conditions enough to capture the soil
images without affecting the actual color
of the soil.
(a) (b)
Fig 12. Image acquisition setup. a) Isometric view of a chamber (b) Styrofoam
box to capture images.

 The soil images are captured using a smartphone camera with 48 megapixels.
 A total of 392 soil images were captured from the collected samples.
 The dataset is made available in the IEEE data portal.
(a) (b) (c)
Fig 13. Represents the clay, loamy sand, and sandy loam soil images captured using a smartphone camera.

Summary
 This work presents the creation soil image database.
 The soil image database created by following standards so that the soil
samples can be labelled accurately.
 The database may help the researchers in identifying the soil types by
image analysis.
 This can enable in advancement of agriculture towards automation.

Objective
 Multi-Feature Fusion for Soil Image Feature Extraction and Classification using
Machine Learning
Problem Statement 2

• Contributions
• Initially 70 soil samples were collected from different regions
of Andhra Pradesh
• The feature fusion technique has been proposed to extract the
soil texture features.
• The GSVM machine learning classifier has been trained to
classify the soil types by varying the kernel parameter.
(a) (b) (c)
Fig. 14. ROI extracted images (a) Coarse sand (b) Fine sand (c) Clay soil
images.
Fig.15. Work Flow of the model

𝐺𝐿𝐶𝑀 =
𝑝 1,1 𝑝 1,2 ⋯ 𝑝 1, Mg
𝑝 2,1 𝑝 2,2 ⋯ 𝑝 2, Mg
⋮ ⋮ ⋱ ⋮
𝑝 Mg, 1 𝑝 Mg, 2 ⋯ 𝑝 Mg, Mg
. . 1
• Calculate the features: contrast, correlation, energy,
homogeneity, and entropy using GLCM [30].
• Calculate the Tamura features [31].
• Gabor filter bank is created by using two scales, six
orientations, and four frequency values [32].
• The ROI images are quantized with hue, saturation, and
value with 6 × 3 × 5 equal bins. The output is a vector
of size 1 × 90.
• The feature fusion vector size for each image is 1 ×
202.
(a) (b)
(c) (d)
Fig 16. a) Confusion matrix of the proposed model. (b) True positive and False-negative rate
of the model. (c) ROC curve of clay and (d) ROC curve of sand.

(a) (b) (c)
(d) (e) (f)
Fig.17. Confusion matrix of GSVM using (a) GLCM (b) Tamura (c) T+GLCM
(d) Gabor (e) Gabor+GLCM (f) Feature fusion model
State-of-
the-art GLCM Tamura
T+
GLCM Gabor
Gabor +
GLCM
Feature
Fusion
GSVM [33] 85 82.5 80 85 95 97.5
Table 1. Ablation experimental analysis
State-of-
the-art GLCM Tamura
T+
GLCM Gabor
Gabor+
GLCM
Feature
Fusion
LR [34] 65 60 90 72.5 62.5 57.5
NB [36] 55 55 57.5 57.5 62.5 70
WKNN [35] 90 62.5 77.5 77.5 90 90
BNN [37] 95 65 72.5 72.5 70 92.5
SVM 42.5 60 52.5 67.5 62.5 87.5
GSVM 85 82.5 80 85 95 97.5
Table 2. Comparative analysis of the state-of-the-art in texture
features and ML classification algorithms

Algorithm Precision Recall Specificity F1-score
Error
rate
Accuracy
(%)
LR 0.55 0.578 0.571 0.564 0.85 57.5
NB 0.8 0.67 0.75 0.727 0.6 70
GSVM 0.95 1 0.952 0.974 0.05 97.5
WKNN 0.9 0.9 0.9 0.9 0.1 90
SVM 0.9 0.857 0.895 0.878 0.125 87.5
B-NN 1 0.869 1 0.929 0.15 92.5
Fig. 18. Confusion Matrix of (a) LR (b) NB (c) SVM (d) WKNN
(e) B-NN (f) GSVM.
(a) (b) (c)
(d) (e) (f)
Table 3. Performance analysis in classifying the soils with state-of-the-art
ML algorithms.
Fig. 19. Comparison of the proposed model with state-of-the-art using 10-fold cross-
validation

Summary
 This work presents multi-feature fusion model for the classification of standard soils
sand and clay using Gaussian SVM.
 The feature fusion model is compared with state-of-the-art machine learning
classifiers.
 This can enable the researchers in advancement and upgrade of techniques for the
classification various soil types.

Objective
Smart Soil Image Classification System using Lightweight Convolutional Neural
Network
Problem Statement 3

Contributions
 Rather than classifying the gravel and aggregate soils, classified the soil types like
sand, clay, loam, sandy loam and loamy sand related to agriculture soils.
 HSV, RGB extraction, and adaptive histogram techniques are applied to the database
to highlight the texture features of the soil sample images.
 Developing a novel Lightweight network aimed to classify the soil images with less
number of layers, learnable parameters, epochs, size of the network, and better
performance.

Fig. 20. The workflow of the proposed model in the classification of soil.

(a) (b) (c)
Fig. 21. a, b, and c represent the clay, loamy sand and sand images and the corresponding ROI images.
(a) (b) (c) (d) (e)
Fig. 22. Sample Pre-processed images. (a) Adaptive histogram [38]. (b-d) RGB channels respectively. [39] (e) V extraction
from HSV.

Fig. 23. Proposed Light-SoilNet network architecture

Class TP TN FP FN Precision Recall F1 score
Clay 146 430 0 2 1.000 0.986 0.993
Loam 90 478 10 0 0.900 1.000 0.947
Sandy
Loam
100 465 0 13 1.000 0.885 0.939
Loamy
Sand
121 454 5 2 0.960 0.984 0.972
Sand 104 476 0 2 1.000 0.981 0.990
Adaptive
Histogram
V
value
RGB
Channels Accuracy
F1 Score
Clay Loam Loamysand Sand Sandyloam
-   81.4 0.83 0.92 0.95 0.64 0.82
 -  78.4 0.79 0.50 0.92 0.86 0.55
  - 92.8 0.94 0.75 0.88 0.95 1.00
   97.2 0.99 0.94 0.97 0.99 0.94
Table 5. Ablation experiment results for preprocessed techniques.
Fig. 24. Confusion matrix and ROC of the Light-SoilNet network.
Table 4. Performance metrics of the proposed model for each class.

Architecture Accuracy (%) Size (MB)
Proposed model 97.20 9.62
MobileNet-v2 [40] 95.45 21.5
MobileNet-v3 94.5 21.1
EfficientNet-B0 [41] 87.54 28.3
ShuffleNet [42] 95.67 14.5
Network
Clay Loam Sand Sandy loam Loamy sand
P R P R P R P R P R
MobileNet-v2 0.918 0.944 1 0.980 0.885 0.920 1 1 1 0.940
ShuffleNet 0.973 0.986 1 1 0.906 1.000 1 0.901 0.913 0.906
EfficientNet-
B0
0.849 0.756 0.500 1 1 0.779 1 1 1 0.984
Light-SoilNet 1 0.986 0.900 1 1 0.981 1 0.885 0.960 0.984
Table 6. Classification accuracy of Light-SoilNet and pre-trained lightweight networks.
(a) (b)
(c) (d)
Fig. 25. Confusion matrix. (a) EfficientNet-B0 (b) MobileNet-v2 (c) ShuffleNet (d) MobileNet-v3.
Table 7. Performance analysis for five soil classes with pre-trained lightweight networks.

Architecture
Accuracy
(%)
Parameters
(Millions)
FLOPs
(G)
Inference
Time (sec)
Training
Time (min)
Size
(MB)
Proposed model 97.20 2.7 3.74 11 16 9.62
Inception-v3 [44] 94.46 24 5.72 6.5 6.5 94.1
Vgg-19 [43] 85.12 144 15.47 7 7.1 519
ResNet-50 [43] 94.46 23 3.87 4.5 6 100
AlexNet [45] 91.00 61 7.27 4.1 7.4 226
Network
Clay Loam Sand Sandy loam Loamy sand
P R P R P R P R P R
AlexNet 0.822 0.923 0.900 1 0.981 0.754 1.000 1 0.889 0.982
Inception 0.918 1 1 1 1 0.779 0.800 1 1 0.984
Vgg-19 0.959 0.833 1 1 0.528 0.651 0.700 1 1 0.685
ResNet-50 1 0.820 1 1 0.962 1 1 1 0.778 1.000
Light-
SoilNet
1 0.986 0.900 1 1 0.981 1 0.885 0.960 0.984
Table 8. Classification accuracy with comparative results.
(a) (b)
(c) (d)
Fig. 26. Confusion matrix. (a)VGG-19 (b)AlexNet (c) ResNet-50 (d) Inception-v3.
Table 9. Performance analysis for the five soil classes with pre-trained DL networks.

Fig. 27. Comparative analysis of F1-score for the soil images.
Fig. 28. Comparison of Light-SoilNet with the pre-trained DL and
lightweight networks using accuracy as cross-validation.

Model
IRSID
Dataset
Accuracy
(%)
Online
Dataset
Accuracy
(%)
(Azizi, 2020)ResNet-50 94.46 68.1
(Azizi, 2020)Vgg-16 92.8 57.4
(Azizi, 2020)Inception-v3 94.46 68.09
(Inazumi, Ph, 2020) 92.72 70.2
Proposed (Light-SoilNet) 97.2 65.7
Table 10. Comparison of the Light-SoilNet and IRSID
dataset with existing models and online dataset.
Summary
• This paper presents a new lightweight CNN-based network
architecture for classifying the five different types of soil images.
• The performance of the model compared with pre-trained
lightweight and DL pre-trained networks.
• The proposed Light-SoilNet architecture outperforms the state-of-
the-art models in classifying the soil images in terms of accuracy,
reducing the learnable parameters, FLOPs, and memory of the
architecture.
• This can enable the researchers in advancement and upgrade of
techniques for the classification various soil types using standard
images.

Objective
 POT-Net: Solanum Tuberosum (Potato) Leaves Diseases Classification using an
Optimized Deep Convolutional Neural Network
Problem Statement 3

Fig 29. Schematic diagram of the proposed POT-Net model

Size/Operation Activation Filter Depth Stride Number of
Parameters
Input (224×224×3) - - - - 0
CL_1 224×224×3 7×7 128 1 18944
BNL_1+ReLU_1 224×224×128 - - - 256
Max Pool_1 112×112×128 2×2 - 2 0
CL_2 112×112×128 5×5 128 1 409728
BNL_2+ReLU_2 112×112×128 - - - 256
Max Pool_2 56×56×128 2×2 - 2 0
CL_3 56×56×64 5×5 64 1 204864
BNL_3+ReLU_3 56×56×64 - - - 128
Max Pool_3 28×28×64 2×2 - 2 0
CL_4 28×28×64 3×3 64 1 36928
BNL_4+ReLU_4 28×28×64 - - - 128
Max Pool_4 14×14×64 2×2 - 2 0
CL_5 14×14×32 3×3 32 1 18464
BNL_5+ReLU_5 14×14×32 - - - 64
Max Pool_5 7×7×32 2×2 - 2 0
CL_6 7×7×16 3×3 16 1 4624
BNL_6+ReLU_6 7×7×16 - - - 32
Max Pool_6 3×3×16 2×2 - 2 0
CL_7 3×3×16 3×3 16 1 2320
BNL_7+ReLU_7 3×3×16 - - - 32
Max Pool_7 1×1×16 2×2 - 2 0
CL_8 1×1×8 3×3 8 1 1160
BNL_8+ReLU_8 1×1×8 - - - 16
FC 1×1×3 - - - 27
Softmax 1×1×3 - - - 0
Contribution
• A novel Optimized deep learning network to classify Potato leaf
diseases using CNN architecture (POT-Net) has been developed to
identify the EB and LB diseased leaves of the potato plant from
healthy leaves with less run-time.
• The proposed model has an optimum number of parameters
compared with the state-of-the-art DL techniques, which reduces
the cost of the model.
• The hyperparameters of the model are optimized using a
metaheuristic algorithm called Whale Optimization Algorithm by
improving the efficiency of the model in identifying the diseased
leaves with an accuracy of 99.12%.
• The proposed model performance has been compared with the
state-of-the-art models, pre-trained DL networks and various meta-
heuristic optimization algorithms to prove the efficiency of the
model.
Table 11. Proposed CNN Architecture with parameters and filter values at each layer.

 Encircling the prey:
𝐷 = 𝐶. 𝑋𝑟 𝑡 − 𝑋(𝑡) … (7)
𝑋 𝑡 + 1 = 𝑋𝑟 𝑡 − 𝐴 ∙ 𝐷 … (8)
 If 𝐴 ≥ 1, then the search agent is far from the optimal solution, and a new search
agent is chosen.
 . If 𝐴 < 1 best solution is achieved by the search agent.
 Exploitation Phase:
𝑋 𝑡 + 1 =
𝑋𝑖 𝑡 − 𝐴 ∙ 𝐷, 𝑖𝑓 𝑝 < 0.5 (𝑆ℎ𝑟𝑖𝑛𝑘𝑖𝑛𝑔 𝑒𝑛𝑐𝑖𝑟𝑙𝑒)
𝐷′
∙ 𝑒𝑠𝑙
∙ cos 2𝜋𝑙 + 𝑋𝑖 𝑡 , 𝑖𝑓 𝑝 ≥ 0.5 (𝑆𝑝𝑖𝑟𝑎𝑙 𝑢𝑝𝑑𝑎𝑡𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛)
… (12)
𝐷
′
= 𝑋𝑖 𝑡 − 𝑋(𝑡)
Whale Optimization Algorithm (WOA) [46]
Fig. 30. Creating spiral shape bubbles by shrinking
the circles to catch the prey.

Model Momentum Epochs L2regularization Learning rate
CNN optimized with WOA 0.7070 6.3717 0.0002 0.0892
CNN without optimization 0.01 8 0.0001 0.01
Class TP TN FP FN Precision Recall F1 score
Early Blight 1056 1772 14 4 0.996 0.987 0.991
Healthy 730 2109 7 0 1 0.99 0.995
Late Blight 1035 1786 4 21 0.980 0.996 0.988
(a) (b) (c)
Fig 31. Sample images. (a) Healthy (b) Early Blight (c) Late Blight [47]
Category No. of images
Training set Validation set
Early Blight 2472 1060
Healthy 1702 730
Late Blight 2465 1056
Total 6639 2846
Table 12. Dataset Description for training and validation.
(a) (b)
Results
Fig 32. POT-Net model (a) Confusion matrix (b) ROC
Table 14. POT-Net model performance parameters and metrics.
Table 13. Training options of the CNN with and without optimization
Model Accuracy (%)
Learnable Parameters
(millions)
Computation time
per image (sec)
Sholihati et al. [48] 91.31 22 0.34
Deepa et al. [49] 88 - 0.6
Network1 93.64 0.3 0.08
Network2 97.26 1.56 0.15
POT-Net 99.12 0.7 0.12
Table 15. Performance analysis with computational time and learnable parameters.

Architecture
Accuracy
(%)
Parameters
(Millions)
AlexNet [45] 97.79 61
DenseNet [33] 96.21 7.2
ResNet-50 [43] 91.64 23
MobileNet-v2 [40] 90.55 3.47
ShuffleNet [42] 98.63 2.29
EfficientNet-B0 [41] 98.77 26
VGG-19 [43] 98.7 144
Non-Opt CNN 97 0.7
POT-Net 99.12 0.7
Network
Early Blight Healthy Late Blight
Precision Recall F1-score Precision Recall F1-score Precision Recall
F1-
score
AlexNet 0.988 0.982 0.985 0.953 0.998 0.975 0.985 0.96 0.972
DenseNet 0.969 0.986 0.977 0.957 0.955 0.956 0.959 0.941 0.949
ResNet-50 0.968 0.934 0.951 0.898 0.915 0.906 0.875 0.898 0.886
MobileNet 0.875 0.971 0.921 0.993 0.839 0.909 0.876 0.899 0.887
ShuffleNet 0.982 0.992 0.986 0.986 0.995 0.99 0.991 0.974 0.982
EfficientNet-B0 0.982 0.986 0.984 0.997 0.998 0.997 0.986 0.981 0.983
VGG-19 0.981 0.992 0.986 0.991 0.991 0.991 0.989 0.978 0.983
Non-Opt CNN 0.974 0.995 0.984 1 0.921 0.958 0.947 0.985 0.965
POT-Net 0.996 0.987 0.991 1 0.99 0.995 0.98 0.996 0.988
Performance analysis with pre-trained DL and non-optimized CNN network
Table 16. Classification accuracy of POT-Net and pre-trained
networks. Table 17. Performance metrics of the pre-trained DL networks.

Optimizer Accuracy (%)
GWO [39] 98.35
SMA [40] 98.91
PSO [41] 97.29
POT-Net 99.12
Performance analysis with MHO algorithms
Optimizer
Early Blight Healthy Late Blight
Precision Recall F1-score Precision Recall F1-score Precision Recall F1-score
GWO 0.983 0.988 0.985 0.981 0.994 0.988 0.986 0.972 0.979
SMA 0.987 0.989 0.988 1.000 0.992 0.996 0.984 0.988 0.986
PSO 0.973 0.982 0.977 0.996 0.953 0.974 0.957 0.979 0.968
POT-Net 0.996 0.987 0.991 1 0.99 0.995 0.98 0.996 0.988
Table 18. Optimizer algorithms performance with proposed CNN architecture.
Table 19. Performance metrics of the proposed CNN with the optimizer algorithms.
(a) (b)
(c)
Fig. 33.. Confusion matrix. (a) GWO (b) SMA (c) PSO.

(a) (b)
Fig. 34. Testing the POT-Net model with real-time images. (a) Confusion matrix (b) ROC
The proposed model has been validated with real-time
images containing 395 images. The model has identified
the EB, LB and healthy leaves images with an accuracy of
81%.

Summary
 This paper uses image phenotyping to classify the potato plant leaves when infected with diseases.
 An image-based framework for identifying the fungal pathogen disease leaves using a CNN
architecture has been presented in this article.
 The proposed POT-Net model performance is evaluated with accuracy, precision, recall, and F1-score
and compared with the pre-trained DL networks and state-of-the-art optimization algorithms.
 The model has achieved high performance in classifying the accuracy is 99.12%, and the time to
process an image to classify the diseased leaves is 0.12 sec.
 In future, by exploring different CNN architectures, the performance will be extended in identifying the
leaf diseases of potato and other plant leaf diseases using laboratory and field images.
 the proposed methodology might be viewed as an effective method for the early detection of diseased
and healthy leaves in potato plants in minimizing the significant agriculture loss, which helps in
automation and continuous monitoring of the fields.

 Digital transformation in Agriculture can meet the food demand of the global population with less
workforce.
 Soil type classification can increase the crop yield by choosing the site specific crops and automation
of the agriculture sector.
 Standard soil image database has been created to classify the soil types using various machine learning
and deep learning approaches by increasing the classification accuracy and minimizing the complexity
of the model.
 To increase the yield of the crop, detecting the plant fungal diseases at early stages was achieved by
using deep learning approaches.
 The performance of the proposed models are compared with other state-of-the-art techniques.
Conclusion

Publications
Conferences
1. D. N. K. Pandiri, R. Murugan and T. Goel, "ODNet: Optimized Deep Convolutional Neural Network for
Classification of Solanum Tuberosum Leaves Diseases," 2022 IEEE Region 10 Symposium (TENSYMP),
Mumbai, India, 2022, pp. 1-6, doi: 10.1109/TENSYMP54529.2022.9864335
Database
1. D N Kiran Pandiri, R Murugan, Tripti Goel, March 18, 2021, "Indian Regions Soil Image Database
(IRSID): A dataset for classification of Indian soils", IEEE Dataport, doi:
https://dx.doi.org/10.21227/2zz3-f173
Journals
1. D. N. Kiran Pandiri, R. Murugan, Tripti Goel, Nishant Sharma, Aditya Kumar Singh, Soumya Sen &
Tonmoy Baruah (2023) POT-Net: solanum tuberosum (Potato) leaves diseases classification using an
optimized deep convolutional neural network, The Imaging Science
Journal, DOI: 10.1080/13682199.2023.2169988 (SCIE, I.F – 0.871)

Works communicated
Journals
1. D N Kiran Pandiri, R. Murugan, T. Goel “Multi-Feature Fusion for Soil Image Feature Extraction and
Classification using Machine Learning,” Signal Image and Video Processing (under Review).
2. D N Kiran Pandiri, R. Murugan, T. Goel “Smart Soil Image Classification System using Lightweight
Convolutional Neural Network,” Expert Systems with Applications (Under Review).
3. D N Kiran Pandiri, R.Murugan, T. Goel “Classification of Soil using Machine Learning and Deep
Learning Techniques–A Review,” IEEE Access (Under Review).

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1st
Semester
2nd
Semester
3rd
Semester
4th
Semester
5th
Semester
6th
Semester
7th
Semester
8th
Semester
Coursework
completed
Comprehensive
Appeared and Literature
Survey started
Literature survey, problem
formulation
Literature survey,
Implementation of problem
statement 1
Implementation of
problem statements 1,
2 using MATLAB
Implementation of
problem statements 4
using MATLAB
Implementation of
problem statement 3
using MATLAB
Synopsis and Thesis
submission
9th
Semester
Implementation of
problem statement 5 using
MATLAB/Python

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You

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