Received: 28 May 2025; Revised: 12 August 2025; Accepted: 27 August 2025; Published Online: 01 September 2025.
J. Smart Sens. Comput., 2025, 1(2), 25207 | Volume 1 Issue 2 (Septembre 2025) | DOI: https://doi.org/10.64189/ssc.25207
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Smart Farming and Crop Protection by Evaluating the
Performance of Convolutional Neural Networks and
YOLOv4 for Plant Leaf Disease Detection
Sushilkumar S. Salve,
*
Sujal Ghone, Sanath Lokhande and Rajesh Pandit
Department of Electronics and Telecommunications Engineering, Sinhgad Institute of Technology, Lonavala, Maharashtra, 410401,
India
*Email: sushil.472@gmail.com (S. S. Salve)
Abstract
Agriculture plays a significant role in India due to population growth and increased food demands. Hence, there is a
need to enhance the yield of crops. Vegetation is frequently susceptible to a wide range of diseases that arise due to
various seasonal and environmental conditions. These plant diseases not only jeopardize the quality and quantity of
agricultural produce but also pose serious threats to farmers’ livelihoods and overall food security. Traditionally, the
identity and treatment of plant diseases have relied closely on guide inspection and professional understanding,
which may be time-consuming and susceptible to human mistakes. With recent advancements in technology, there
is a growing interest in automated disease detection systems that leverage artificial intelligence and machine learning
techniques. These contemporary solutions provide faster, more accurate and cost-effective techniques for identifying
plant diseases, permitting farmers to take properly timed preventive and corrective measures. This study presents a
novel approach to plant leaf disease detection and severity classification by leveraging the capabilities of YOLOv4 and
Convolutional Neural Networks (CNNs). These machine learning algorithms have proven great potential in image
processing and pattern recognition tasks, making them appropriate for diagnosing plant situations from visual
information. We have used a dataset containing images of four various plant species, each suffering from different
kinds of infections. By training these models by available datasets, the proposed system can recognize and classify
diverse plant diseases with high accuracy. The performance parameters are evaluated extensively and results are
derived. The accuracy of the CNN and YOLOv4 obtained around 95.5% & 91.0%, respectively.
Keywords: Plant disease; Severity classification; Convolutional neural networks; Support vector machines.
1. Introduction
Agriculture plays a vital role in the Indian economy providing food security for its large population, employing a
significant portion of the workforce, contributing to the country's Gross Domestic Product(GDP). Therefore,
maintaining the quality, quantity, and health of crops is essential. Due to the unexpected rise in food consumption
caused by a population explosion, plant diseases have become a major concern in every country.
[1]
Based on the latest projections from the United Nations Food and Agriculture Organization (FAO), plant diseases cause
over 40% of the world's vegetation to die every year. Numerous seasonal variables, animate (weeds and pests), and
inanimate (weather, rainfall, wind, and moisture) factors can all cause different types of diseases to infect the plants.
Plant disease detection can aid farmers in diagnosing and treating comparable conditions, which will improve food
safety and profitability. One of the factors affecting the quality and quantity of agricultural production is the influence
of plant disease.
[2]
Recent trends present new opportunities for agricultural imaging. It is possible to identify folate diseases and show the
health of plants by examining the visually noticeable patterns of plant leaves. As a result, it provides a means to
significantly decrease yield loss and boost plant production.
[3]
Many machine learning methods have been employed
extensively to detect various plant diseases based on infected leaves. To improve accuracy, this research employs deep
learning models such as Dense Net and Efficient Net to detect plant diseases and identify their severity, using a support
vector machine. Farmers implement significant mitigations as soon as possible since they are aware of how serious
the disease is.
[3]
Most of the literature on plant diseases looks at them from a biological standpoint. Their predictions are based on the
exposed leaf and plant surfaces. Finding the first signs of disease is one of the most crucial steps in properly managing
it. Human experts have historically detected. Blind diseases can be identified by human doctors, despite some obstacles
that may hinder their efforts. In this setting, plant disease prevalence has a detrimental effect on agricultural
productivity if infections are not identified quickly.
[4]
It is crucial for managing agricultural output and decision-
making.
Plant disease identification has grown to be a significant challenge in recent years. Infected plants typically exhibit
visible flaws or lesions in their leaves, branches, blooms, or fruits. Variations in each disease or pest state can typically
be recognized by their distinct visual patterns. Plant leaves are the primary source of information on plant diseases
because most disease symptoms may initially manifest on the leaf.
[5]
2. Related work
This phase describes various techniques used for detecting diseases in plant leaves. The images provided as an entry
are the only basis on which plant diseases are identified. It is well recognized that images may contain noise, which
might lead to inaccurate training results. Techniques like background removal and segmentation algorithms have been
employed to assist cleaning the noisy historical pictures to achieve greater efficacy. This method is suggested in the
study,
[6]
which lists the several deep models that have been investigated and used on sets of images with certain
backgrounds.
For the domain of plant diseases, most researchers heavily rely on methods like Deep Learning (DL) algorithms. The
following studies compare the use of DL approaches. Saleem et al.
[6]
explains DL patterns that were popular. It presents
the most recent developments and difficult situations for the detection of plant leaf disease using advanced imaging
techniques and in-depth study, in addition to the issues that need to be resolved. Similarly, Zou et al
[7]
provides insight
into the evaluation of several deep learning architectures. The visually appealing version was also fine-tuned using a
variety of optimization techniques. As a result, the version of the Exception that was used. To identify plant disease
leaf images as a statistics set, Majeed et al.
[8]
that was referenced to here provided an overview of the exemplary
evaluation, frameworks, Convolutional Neural Network (CNN) styles, and optimization strategies. It emphasized
virtues and drawbacks, making it easier for developers to use DL approaches.
Models which provided higher accuracy were widely used by researchers in plant disease detection. Junction extraction
is used to get better results with neutral community models while using low computer resources than conventional
models. Its average accuracy of 94.8% shows that it is effective even in unfavorable circumstances. Majeed et al.
[8]
put out a model that is predicated on the residual connection and inception layer. An image processing structure
comprising three stages like image segmentation, Feature extraction, and classification was used to identify and
classify plant diseases. The multi-threshold, and other techniques were used in the trials, which were conducted on
four distinct tomato leaf sections. This approach had a 10-fold cross-convenience and an overall accuracy of 98.3%.
The primary objective is to inform users of the diagnosed disease name and direct them to an online marketplace where
they can purchase pesticides for the ailments and use them exactly as prescribed. In this study, Support Vector Machine
(SVM) and Artificial Neural Network (ANN) are used to choose two plants such as corn and tomato for disease
identification and alerts the customers of the ailment. SVM attains 62–73% accuracy, while ANN attains 85%. Table 1
summarises the related wok used for detecting diseases in plant leaves.
Table 1: Summary of related works.
Source
Dataset/Crops
Methods/Results
KC et al.
[1]
Garden Village
Exception with Adam; 99.7%
Saleem et al.
[6]
Garden Village
VGG16;94.8%
M. Bhagat et al.
[3]
Leaf of the Plant
Densenet121(Removed Background); 93%
Dhaka et al.
[4]
Tomato
SVM;98.3%
Ananthi et al.
[5]
Garden Village
DL models; 95%
Rinu et al.
[34]
Tomato and corn
SVM; 60-80%
Hassan et al.
[35]
Fruits e.g., Apple
CNN;70-80%
Kaur P. et al.
[18]
Garden Village
GAN
Zou K. et al.
[7]
Plant Village,
Cassava and Rice
Around 99%for garden Village and rice,75% for
cassava
3. Dataset
Images of plant leaves from Plant Village were used to see the overall performance. A total of 65,345 plant leaves,
including both healthy and diseased samples, were collected and are known as the Plant Village Dataset (Plant Village).
Apple, blue berry, etc. are among the fourteen amazing crop varieties that are included in the databases. A selection of
example photos is shown in Fig. 1, which displays the number of image files collected for lesion diagnosis and detection.
The availability of water, vitamins, microorganisms, viruses, and fungus are examples of common stressors that lead to
sickness.
[9]
Fig. 1 shows sample examples of plant leaf images. In this study, we have used the Garden Village database.
[10,11]
This
dataset contains 58,432 images of 13 different plants, divided into 40 categories of healthy leaves and various types of
diseases. This study used 32,878 photos of 8 kinds of vegetation, apples (9,123 pics), corn (8,987pics), potato
(4,898pics), tomato (11,125 images), and rice (123).
To compare individual sample of plant leaf images taken from dataset are feed to deep learning models. The process
includes classification, feature selection, feature extraction, and preprocessing. Because inaccurate data in a dataset
might alter the appearance of a test, the information series technique is essential in real-time operations. As a result, it
is essential to follow the unusual norm and standard while gathering statistics. Subsets of the datasets are created using
an 80:20 training-to-testing ratio. While the last twenty-seven demonstrate unique plant leaf diseases, thirteen of the
forty trainings that comprise our information are the healthful classes.
Fig. 1: Sample examples of plant leaf images.
The data have 256×256-pixel RGB images showing results of leaves. Based on their image classifications, care was
taken during the photo shoot to ensure that each image captured a single centered leaf. Additionally, the environment
for shooting photos and the lighting are consistent. It is significantly more beneficial to ask questions about how to use
the knowledge effectively after analyzing a variety of data.
[11]
Fig. 2 shows block diagram of plant leaves disease
detection mechanism.
Fig. 2: Block diagram of plant leaves disease detection mechanism.
4. Pre-processing and augmentation
It is generally known that different types of factors, such as human error and noise, can be the facts obtained from any
source. The set of rules can produce misleading results if it uses such data immediately. Pre-proclamation of the facts
supplied is therefore a latter step. Pre-processing techniques include scaling, color space modification, picture
enhancement, and noise reduction to improve the quality of the data and eliminate or minimize noise from the original
input data. The act of hybrid model is evaluated in this study by enlarging the leaf picture to 224 × 224 x 3.
[11]
Additionally, it is significantly more beneficial to ask questions about how to use the knowledge. Fig. 3 shows the
outcomes of preprocessing of the RGB images (plant leaves) using gray scale transformation. Records augmentation,
which includes flips, zoom, vertical shift, and horizontal shift, is essential for training information since it increases
the number of photos and reduces overfitting.
This is used encoder-decoder architecture and highly defined neural networks to apply semantic leaf disease division
to a collection of plant pictures. Three distinct semantic segmentation models like Lonate-34, Pyramid Scene Parsing
Network (PSPNet), and Seagate
[12]
were employed to detect wounds to provide a high density. After the lesions are
detected, they are classified using several classifiers.
Fig. 3: Grayscale conversion of input dataset.
The plant blades are the input for both semantic segmentation techniques. PSPNet,
[13]
Seagate,
[14]
and Longett-34,
[15]
are two semantic segmentation models, are used to recreate model. To utilize global reference information, this
modular semantic partition paradigm uses reference aggregation based on many domains. Local and global cues work
together to strengthen the final restriction. Moreover, the U-Net architecture is extensively recognized for its
effectiveness in the responsibilities of semantic division, creating the foundation of this technique. It can divide a wide
range of gadgets, which include clinical imaging for PC pictures and prescribed in satellite TV. By combining decoder
and encoder approaches, the Llenge-34 design aims to enhance partition model training and increase productivity and
efficiency. It consists of a down-sampling server which shrinks input photos to exclude top level information generate
predictions at the pixel level. Linknet-34 connects the coder and decoder via a jump connection. In addition, low-level
features may be communicated without delay into decoders and integrated with high-degree statistics via the use of
jump connections, akin to U-Net. Division is contemplated inside the object. When it comes to segmental segmentation
problems, the PSP Net plays properly. By assigning a semantic label to a given image, each pixel attempts to share the
semantic segmentation of the image into regions corresponding to different types of objects. Pyramid basin modules
are used by the PSPN to acquire multi-paan reference facts from wonderful components of the doorway picture.
[16]
This
makes it easier for the model to assume more pixels than are necessary, particularly for objects of different sizes. To
collect contextual records, PSP Net repeatedly down samples the input characteristic map using a pyramid shape and
uses global pooling at various scales. As an alternative, the conventional layers are used for characteristic combining
and up sampling. Examples of jobs where PSPNET performs well and where pixel-level segmentation is required are
Sean Parsing, Image Segmentation, and Pleasant-Green Object recognition. With a total of 26 convolutional layers,
Seg-Net is an encoder-decoder version. The VGG16 community's development and contraction routes have thirteen
Convo layers. The encoder and decoder networks are separated by two fully connected (FC) layers. The Rectified
Linear Unit (ReLU) is the system that is used to easily and quickly construct function mappings.
A max-pooling operation with a stride of two comes after each layer for the down sampling of the feature map. Down
sampling increases the number of channels and filter banks, typically doubling them at each step. Each encoder layer
has a corresponding decoder layer, where the decoder samples the data by a factor of two before passing it to the next
feature map. The primary encoder handiest has a multichannel characteristic map, but the decoder has just three
channels. After map output, a multi-dimensional feature is employed to solve a 2-class problem by using the Sign ID Ed
capability to separate plant pixels from the background.
[17]
4.1 Image acquisition
The input information first is captured the use of a Xiaomi USB 2.0 HD webcam that supports taking pictures video
datasets up to 720p and a body fee of 30 frames per 2d (fps). This enters records then undergo photograph pre-
processing where the statistics are normalized into a scale of [0,1] because it consisted of pixels starting from 0-255. Upon
normalization, the performance of the CNN model improves ensuring better numerical balance and quicker
convergence. The input records also undergo grayscale conversion as weed detection is predicated greater upon shapes
and textures than color.
[18]
The machine is made greater efficient by way of resizing the facts to 64×64 pixels for that
reason reducing the photograph size and reducing the computational fee.
[19]
4.2 Feature extraction
Now functions are being extracted from the pre- processed photo using 2D Convolution that extracts out all critical
features and styles like edges and so forth.
[13]
It applies 32 filters of the dimensions (3×3 i.e., 64×64 in grayscale) at
the input image. The 2d convolutional layer once more applies to 64 filters of the equal size. Rectified Linear Unit
(ReLU) right here acts because the activation function which converts the terrible values to zero therefore introducing
non- linearity. Fig. 4 shows demonstration of the Rectified Linear Unit (ReLU).
Fig. 4: A demonstration of the Rectified Linear Unit (ReLU).
The non-linearity added through the ReLU activation feature lets in the CNN network to learn extra complex patterns
and functions which are beyond the linear relationships. This makes the network computationally extra green as fewer
neurons prompt immediately, enhancing generalization, appearing as simple threshold capability. Compared to other
activation functions such as sigmoid and tanh, it avoids costly exponential calculations, thereby enabling faster
convergence during network training and helping gradients remain large during backpropagation. Fig. 5 illustrates how
negative inputs are converted to zeros, introducing sparsity in the activations.
[20]
5. Proposed methodology
We begin by outline the dataset, emphasizing the splitting processes, and discussing the methods of training models.
Also, the suggested model's records flow diagram is displayed in Fig. 5.
[21]
5.1 Dataset description
The proposed experiment applied the Garden Village Dataset present in Kaggle which includes 20,639 photos of high
decision of 38 exceptional healthful and diseased leaves bearing on 18 different species of plants. The model
implementation considers segmented images of four plants along with their diseases.
5.2 Image segmentation
One crucial aspect of image processing is image division. There are several techniques for dividing pictures, including
the Otsu method, K-peins clustering, borders and spot detection algorithms, etc. One of the best edge detectors is the
Edge Detection as it offers the best, most dependable, and least error-prone real age point detection.
The following procedures are used to identify edges with the clever edge detector:
1. Smoothing: order to smooth the photo and minimize noise, Gaussian clear out is used.
2. Finding depth gradients: Wherever the picture's gradients have significant magnitudes, the edges are indicated.
Large- magnitude photos' gradients are emphasized as edges.
3. Non-maximum suppression: This technique eliminates erroneous reactions to component detection.
4. Double Threshold: This is a criterion used to determine true edges and abilities.
5. Edge tracking: The weak edges attached to the strong edge are the original or the actual edge, while the weak edges
that are not attached to the strong edge are pressed.
Table 2: Data specifications.
Plant
Disease Name
Count
Corn
Gray leaf spot
443
Maze rust
2193
Fit (Healthy)
1162
Apple
Bacterial black rot
621
Healthy
1645
Apple Scab
630
Tomato
Septoria
1771
Early blight
1000
Leaf mold
952
Healthy
1591
Grapes
Esca
1383
Siriasis
1076
Black rot
1180
Healthy
423
Fig. 5: Flow diagram of plant leaf disease classification mechanism.
6. Classification
6.1 Support Vector Machine (SVM) algorithm
Plant diseases are identified using the Support Vector Machines (SVM)
[22]
set of rules. Finding the Most Marginal
Hyperplane (MMH) that divides the educational records into instructions is the aim of supervised mastering and vector
space-based machine learning techniques like SVMs. This method facilitates the examination of statistics for
regression analysis, classification, and grouping. The following are the steps to determine the biggest marginal
hyperplane:
1. Flat are recursively generated to segregate the training in the exceptional manner.
2. The following step is to select for proper outcomes the hyperplane with the highest segregation from each nearby
statistics component.
6.2 Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNNs) are a class of Artificial Neural Network (ANN) designed to automatically and
adaptively learn the features at different levels of detail from the input data (generally images). Image processing and
recognition are two main applications of CNN. They are carried out by means of an optimizer and activation
capabilities.
[23]
DenseNet: One shape of CNN that employs dense connections between layers is referred to as a Dense Net. These
layers are linked to each other through Dense Blocks. If there are similar plant picture variants, this technique is
appropriate. Apple and tomato leaves, as an instance.
[24]
Efficient Net: A specific predetermined set of scaling coefficients is used by the CNN structure Efficient Net to evenly
scale each dimension using the compound coefficient approach. It attains greater precision and efficiency.
Activation functions: The activation function in neural networks overseas transforming the weighted sum of inputs from
nodes in a community layer into an output. ReLU (Rectified Linear Unit) is used in this model because the hidden
layer's activation characteristic outputs the input instantly if it is of good quality; else, it would output 0
f(x)=max (0, x)
The SoftMax function, which calculates the likelihood of data pertaining to the magnificence used for the multiclass
concerns and returns values between 0 and, is utilized in the concluding layer:
Optimizers: Optimizers are techniques for changing a model's parameters, such as weights and study rate, to reduce
losses. Adam Optimizer is one of the optimizers utilized in this version. This set of rules accelerates the gradient
descent process by considering the exponentially weighted common of the gradients.
[25]
Optimizers are techniques used to adjust the parameters, including weights and learning rates, of a model to minimize
loss. One of the optimizers employed in this model is the Adam Optimizer, which accelerates the gradient descent
process by computing an exponentially weighted average of past gradients.
Pyramid Scene Parsing Network (PSPNet) performs exceptionally well in challenging semantic segmentation
scenarios. Fig. 6 shows object segmentation using semantic segmentation of the image. Additionally, the semantic
segmentation input seeks to distinguish images by assigning a semantic label to each pixel. Pyramid pooling modules
are used by PSPNet to extract various scale data from various regions of the original picture. This enhances the pixel
forecasting capability, particularly for objects with varying sizes. PSPNet frequently down samples the input feature
map and applies global pooling at multiple scales to capture contextual information. Pixel-level predictions are then
generated by up sampling and combining this information. While PSPNet uses global pooling across several pyramid
levels to gather context, it does not include the fusion step that is present in Feature Pyramid Network (FPN). Function
combining and up-sampling are replaced with convolutional layers.
[12]
Fig. 6: Object segmentation using semantic segmentation of the image.
6.3 Convolution operation
All the layers of the CNN Network Densnet121
[26]
include feed-forward coupling. Each feature map from the earlier
layers serves as the layer's input. The N-layer network is regarded as an example. Additionally, the feature map is then
forwarded to the Nth layers' subsequent N input. Consequently, the network's dense connection is represented as an
input by Equation (1) Layer.
(1)
in which
and Li is the layer's input. Feature maps from 0 to L-1 and together make up the
layers. "Density block (DB)"and "infection blocks (TB)" are the two main types of building networks. Each of the
"dense layers (DL)" that make up the dense blocks consists of a layer that is 1x 1 conv and a layer that is 3 x 3 conv.
CNN architecture is known as an aggregated residual transform network, or briefly considered, the ideas of Inception
Networks and Residual Networks (DenseNet). It provides the imagination of change and combines, highlighting the
notion that cardinality may enhance impact. Dense Net employs a split, re-creation, and merging block that
incorporates several ameliorations. As a result of these modifications, a huge number of representations and a broad
spectrum of abstractions were captured. The transition blocks' 1 x 1 CNN layers and 2 x 2 Pooling layers are situated
dense blocks. The layers of batch normalization are arranged in descending order. DenseNet121 is a variant of the
Dense Net architecture, which utilizes convolution operations applied to feature maps with real-valued inputs.
[27]
Fig. 7 shows 2D convolution level function of a CNN.
These layers in each DenseNet121 are illustrated in Figs. 7 and 8. Convolution is an operation that uses real numbers
as parameters and is applied to two features. The convolution process involved a multidimensional vector (tensor).
The kernel, which is adjusted during training, is essentially a multidimensional parameter tensor. For instance, a two-
dimensional kernel represented by K is frequently employed when image I is used as input.
Fig. 7: 2D Convolution level function of a CNN.
Fig. 8: Maximum clustering of left and middle-right pooling in a pooling function.
1. Pooling: A CNN typically consists of three main layers. The functioning of the entry facts is folded at the community
level. This phase is sometimes referred to as the thought level.
[28]
The last stage is the be-part feature, which substitutes
a statistical precis produced by the previous neural network layer position for the prior output or network output. The
implementation of the 2×2 pooling method is depicted in Fig. 8. There are two types of pooling: Maximum Pooling
and Average Pooling. Max pooling selects the highest value in each region as illustrated in Fig. 8, by the 2×2 Pooling
method. This reduces the data size by a factor of 4. Average pooling, on the other hand, computes the arithmetic
meaning of the values in the region.
[29]
2. Model and Architectures: The model was chosen to address the specific problem, taking into account the many
possible designs available and the widespread use of convolutional neural networks in various modern computer vision
and predictive tasks.
[13]
It is also important to consider the trade-off between the number of layers and parameters to
train (and the computational expenses that are required for training), especially the ones that have been well established
in the literature and have shown state-of- the-art performance in computer vision applications. In this regard, we chose
to use convolutional neural networks with fewer parameters than both popular solutions in the literature, such as the
standard for our tasks. The resulting architectures used are therefore:
1. LeNet: Designed for the purpose of handwritten digit recognition, this network has two layers of convolution which
are pooled using max pooling to get features. Finally, to the outlet category, we apply a final convolutional layer using
dense layers.
[14]
2. AlexNet: This Network consists of five preliminary convolutional layers. screen 3 layers which only two layers Z-
layers is not fully attached within the quit to provide the classification. It aims to use convolutional, neural network
architecture with right overall performance mentioned in the corresponding studies.
3. MobileNet: This convolutional neural network is designed on deep separable convolution operations, which
reduces the burden of workload to execute the internal operations the initial layers of this mobile- targeted devices and
embedded devices.
[26]
4. ShuffieNet: It is primarily built upon two operations, which the authors defined: the so-called the group
convolutions that could be foreroi4ing, and may be multiple convolutions on part of the input channels, and the channel
shuffie, which uses a random blend the output channels of the convolutions inside the organization. This structure,
advocates say supports a reasonable accuracy with a low computing cost.
[30]
5. EffNet: Along the lines of utilizing in-depth separable convolution, which is akin to MobileNet design.
[30]
and
ShuffieNet networks; however, it presents a new convolution block that reduces the computational cost and
outperforms state-of-the-art for certain known databases.
[31]
6. Sheaf Attention Network: Efficient segmentation and classification using Convolutional neural networks with Sheaf
Attention Networks (CSAN).
[32]
7. Results and discussion
It should be noted that the total number of parameters is smaller than that of the VGG16 and VGG19 designs,
[33]
indicating that, under the circumstances examined, we may find that their combination may end up being less expensive
than well-known architectures in the literature. The architecture under consideration was trained using the previously
described methods, and their individual performance in the test set was assessed.
In supervised learning, a collection of instances (input and intended output) is presented to train the classifier. In
supervised methods, the model is trained to produce the desired output but from a training set. This training data set
consists of correct and incorrect outputs so that the model can evolve. According to this information, it is expected that
the classifier should operate in a linear or non-linear fashion and accurately predict the output for newly input data.
Considering the SVM, they use an ideal separation hyperplane that is precisely in the middle of the two classes margins
to partition the feature. Nonlinear Radial Basis Function (RBF), quadratic, polynomial, Gaussian and two-layer
perceptions are non-linear functions used in the SVM analysis. This process is for improving the separation margin.
After that, an SVM classifier is used to analyses the images to identify the plant. The illness classifier and severity
classifier modules employ deep learning techniques to improve performance and get more precise findings.
7.1 Using deep learning (Image-based approach):
The recent most popular and efficient algorithm is Convolutional Neural Network (CNN). It will be trained to
categorize leaf images into groups that are healthy or ill.
Technologies & Tools:
• Python 3.13.3
• TensorFlow/Keras/PyTorch
• OpenCV(for preprocessing)
• Pre-trained Models like MobileNet, ResNet for transfer learning
Workflow:
• Dataset
• Plant Village Dataset.
• Preprocess Images
• Resize
• Normalize
• Augment
• Classification
• CNN Model (a pre-trained model)
• Evaluation metrics
• Accuracy
• Confusion Matrix
• F1- score
7.2 Evaluation metrics
[32]
The outcome of plant leaf lesion detection is tested with various evaluation criteria, such as precision, recall, F1-score,
accuracy, Jaccard, and dice coefficient. The precision (Pre) in Equation (2) is the ratio of the correctly detected targets
to all the targets detected by the proposed model.
(2)
where,
= True Positive
= True Negative
= False Positive
= False Negative
Recall, often called 'Rec' is the percentage of targets that the model got right. You can find the recall rate using the
formula in Equation (3). 'FN' means that the model didn't correctly identify the target, which in this case is leaf lesions.
(3)
The F1 score is another way to measure how well a classification model is doing, taking both memory and precision
into account. It gives a good picture of both Precision and Recall since it combines them into one score. It is greatest
when Precision and Recall are identical, according to Equation (4).
(4)
Accuracy is one metric used to evaluate classification methods. Accuracy may be defined as the proportion of accurate
predictions our model produced. Accuracy is defined formally as the ratio of correctly labelled pictures to all sample.
Accuracy is represented mathematically in Equation (5).
(5)
The intersection of spatial overlap is measured using the union size of two label sets, based on the Jaccard Index (JAC)
in Equation (6).
(6)
The Dice Similarity Coefficient (DSC) shows how similar two binary images are, with zero meaning no overlap and
one meaning complete overlap. We get the segmentation result and DSC values from a specific Equation (7).
(7)
The model classifies many images by predicting how likely it is that each image falls into a particular class.
7.3 Experimental analysis
7.3.1 Metric value
The proposed prototype in this study is trained and developed using a standard self-developed dataset. The prototype
was being implemented using CNN as well as YOLOv4 deep learning algorithms and after successful testing phase,
the results have been concluded and compiled evaluation metrics.
The results of each technique have been thoroughly evaluated ensuring untampered standards and accurate real-world
simulation. Table 3 summarizes the result metrics of YOLOv4 technique that was implemented on the very same setup
for a through comparison. Fig. 9 shows results using YOLOv4 technique.
Table 3: Results during field testing using YOLOv4.
Field Trial
True Cases
False Cases
% Error
% Success
TN
TP
FN
FP
1
6
11
3
0
15
85
2
8
9
3
0
15
85
3
11
9
0
0
0
100
4
7
11
2
0
10
90
5
10
10
0
0
0
100
6
3
16
0
1
5
95
7
8
11
1
0
95
8
6
9
3
2
25
75
9
14
6
0
0
0
100
10
6
11
0
3
15
85
Total
79
103
12
6
-
-
Average
-
-
-
-
9.0
91.0
Fig. 9: Results of field trail using YOLOv4.
Using Equation (5) we can calculate the value of accuracy as follows:
Similarly, using Equation (2) and (3) the precision and recall calculated
Now, Equation (4) is being used to calculate the F1 Score for technique:
= 91.96%
Table 4: Results during field testing using CNN.
Field
Trial
True Cases
False Cases
% Error
% Success
TN
TP
FN
FP
1
6
11
2
1
15
85
2
6
12
2
0
10
90
3
6
12
0
2
10
90
4
3
16
0
1
5
95
5
7
12
1
0
5
95
6
11
9
0
0
0
100
7
8
12
0
0
0
100
8
4
16
0
0
0
100
9
14
6
0
0
0
100
10
10
10
0
0
0
100
Total
75
116
5
4
-
-
Average
-
-
-
-
4.5
95.5
Fig. 10: Results of field trail using CNN.
Table 4 summarizes the result metrics of CNN technique that was implemented on the very same setup for a through
comparison. Fig. 10 shows results using CNN technique.
Similarly, using Equation (2) and (3) the precision and recall calculated
Now, Equation (4) is being used to calculate the F1 Score for technique:
= 96.25%
From the above equations, it can be clearly observed that the CNN technique has completely outperformed the
YOLOv4 algorithm and proven its proficiency in accurate object detection and recognition. This study was to
determine the optimal performance of various deep learning (DL) algorithms in classification and precise elimination
of weeds amongst the crop field.
CNN classification and YOLOv4 supervised algorithms for a comparison-based study and detailed analysis in the
search for the best algorithm to be implemented.
Fig. 11: Confusion Matrix for YOLOv4 technique.
Fig. 11 shows the Confusion Matrix for YOLOv4 technique YOLOv4 in field testing lacks true positive cases (TP =
103), whereas its greater true negative (TN = 79) and false negative (FN = 12)
Fig. 12: Confusion Matrix for CNN technique.
Fig. 12 shows confusion matrix for CNN technique. It could be truly determined that the CNN method has absolutely
outperformed the YOLOv4 algorithm and confirmed its scalability in correct object detection and recognition. The
YOLOv4 in field testing lacks true positive cases (TP = 103), whereas its greater true negative (TN = 79) and false
negative (FN = 12) values result in lower precision as compared to CNN.
[20]
Traditional ML + Image Features:
• Use models like SVM, Random Forest, KNN for classification.
• Extract features like color histograms, shape descriptors, texture.
Web App Integration:
Once we have a model, we have:
• Deploy via Flask backend.
We looked at different plant disease datasets from Kaggle, including apples, corn, tomato, and grapes. The test scores
for the disease and severity classifiers are shown in Table 5.
This study was to determine the optimal performance of various deep learning (DL) algorithms in classification of
plant leaf diseases. CNN classification and YOLOv4 supervised algorithms for a comparison-based study and detailed
analysis in the search for the best algorithm to be implemented.
Table 5: Test scores for classifying plant diseases.
Plant
Disease name
Accuracy (%)
Apple
Healthy
92.5
Apple scab
100
Black rot
100
Corn
Healthy
100
Common rust
100
Gray leaf spot
57.5
Plant
Disease name
Accuracy (%)
Grapes
Healthy
90
Black rot
High
100
Low
85
Esca
High
90
Low
100
Siriasis
High
95
Low
100
Tomato
Healthy
100
Leaf mold
100
Septoria
100
Early blight
95
Fig. 13: Homepage of graphical user interface.
Fig. 14: User interface of proposed prediction model.
7.4 Discussion
Deep learning (DL) is changing the scenario when it comes to diagnosing plant diseases using digital images. To get
the right response quickly, the models need to be fast and accurate. The design of the network chosen will depend on
to improve or reduce accuracy of prediction model. If we need to change things up often, Dense Net (CNN) is the
quickest option, though it can be a bit unstable. On the other hand, if we want top-notch accuracy, we might want to
look at GoogLeNet or AlexNet. They found that while InceptionV3 had the lowest accuracy in their tests, AlexNet,
though not performing as well as expected, still outperformed it overall.
Table 6: Comparison with other detection techniques.
Model Name
Accuracy (%)
VGG16
86.21
GoogleNet
79.23
AlexNet
80.09
ViT
89.09
YOLOv4
91.00%
CNN
95.50%
The prototype in this study is being developed and implemented using CNN classification and YOLOv4 supervised
learning algorithms for a comparison-based study and detailed analysis in the search for the best algorithm to be
implemented. This step is particularly necessary for accurate classification of plant leaf diseases detection on different
geographical locations and regions. Fig. 15 shows the accuracy comparison by various detection techniques. Fig. 16
shows the results of comparision between YOLOv4 vs CNN.
Fig. 15: Accuracy comparison by various detection techniques (deep learning algorithms).
Fig. 16: Results of YOLOv4 vs CNN.
Now as we observe in Table 6, a comparison has been stated amongst accuracies of four other methods being generally
used in effective object detection and classifications with the two root methods mentioned in this study. Jun Zhang et
al.
[31]
mentioned in his study about the higher accuracy of the original ViT model due to its stronger sequence modelling
abilities and unique capabilities to capture long-range dependencies. But when we carefully consider both CNN, ViT
and YOLOv4 in a comprehensive way, then the CNN model due to its better balance for local and global features,
results in an overall better performance and improved classification.
Additionally, despite having several depth layers, the deepest networks are ResNet50, ResNet101, and InceptionV3
are showed comparatively low accuracy. Lastly, a time and performance analysis of several CNNs was suggested.
8. Conclusion and future scope
The main goal of the plant disease detection and severity classification model is to uses images of leaves of infected plant
species to precisely identify plant diseases and their severity levels. This model focuses on using advanced image
processing techniques based on CNN to determine multi-class detection of plant leaf disease detection, which helps to
extract important characteristics required for efficient categorization. Utilizing these extracted metrics, the model
facilitates early and accurate disease identification in a variety of plants, allowing producers to take prompt and
suitable corrective action. Apple, corn, grapes, and tomatoes are the four plant species on which the system has been
extensively tested; each of these plant species has two to three different diseases. This makes the model approachable
and useful for actual agricultural applications by enabling effective and straightforward forecasts. Both YOLOv4 and
CNN-based models exhibit notable gains in plant disease detection accuracy, according to the experimental
investigation. The addition of severity categorization improves the model's usefulness in practice by revealing
information about the disease's course in addition to its identification.
Conflict of Interest
There is no conflict of interest.
Supporting Information
Not applicable
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing
or editing of the manuscript and no images were manipulated using AI.
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