An enhanced lightweight model for apple leaf disease detection in complex orchard environments

Abstract

Automated detection of apple leaf diseases is crucial for predicting and preventing losses and for enhancing apple yields. However, in complex natural environments, factors such as light variations, shading from branches and leaves, and overlapping disease spots often result in reduced accuracy in detecting apple diseases. To address the challenges of detecting small-target diseases on apple leaves in complex backgrounds and difficulty in mobile deployment, we propose an enhanced lightweight model, ELM-YOLOv8n.To mitigate the high consumption of computational resources in real-time deployment of existing models, we integrate the Fasternet Block into the C2f of the backbone network and neck network, effectively reducing the parameter count and the computational load of the model. In order to enhance the network’s anti-interference ability in complex backgrounds and its capacity to differentiate between similar diseases, we incorporate an Efficient Multi-Scale Attention (EMA) within the deep structure of the network for in-depth feature extraction. Additionally, we design a detail-enhanced shared convolutional scaling detection head (DESCS-DH) to enable the model to effectively capture edge information of diseases and address issues such as poor performance in object detection across different scales. Finally, we employ the NWD loss function to replace the CIoU loss function, allowing the model to locate and identify small targets more accurately and further enhance its robustness, thereby facilitating rapid and precise identification of apple leaf diseases. Experimental results demonstrate ELM-YOLOv8n’s effectiveness, achieving 94.0% of F1 value and 96.7% of mAP50 value—a significant improvement over YOLOv8n. Furthermore, the parameter count and computational load are reduced by 44.8% and 39.5%, respectively. The ELM-YOLOv8n model is better suited for deployment on mobile devices while maintaining high accuracy.

Full text

An enhanced lightweight
model for apple leaf disease
detection in complex
orchard environments
Ge Wang
1
, Wenjie Sang
1
*, Fangqian Xu
1
, Yuteng Gao
1
,
Yue Han
1
and Qiang Liu
2
1
College of Intelligent Equipment, Shandong University of Science and Technology, Taian, China,
2
Technology Department, Shandong Xinhua'an Information Technology Co., Ltd., Qingdao, China
Automated detection of apple leaf diseasesiscrucialforpredictingand
preventing losses and for enhancing apple yields. However, in complex natural
environments, factors such as light variations, shading from branches and leaves,
and overlapping disease spots often result in reduced accuracy in detecting apple
diseases. To address the challenges of detecting small-target diseases on apple
leaves in complex backgrounds and difficulty in mobile deployment, we propose
an enhanced lightweight model, ELM-YOLOv8n.To mitigate the high
consumption of computational resources in real-time deployment of existing
models, we integrate the Fasternet Block into the C2f of the backbone network
and neck network, effectively reducing the parameter count and the
computational load of the model. In order to enhance the network’s anti-
interference ability in complex backgrounds and its capacity to differentiate
between similar diseases, we incorporate an Efficient Multi-Scale Attention
(EMA) within the deep structure of the network for in-depth feature extraction.
Additionally, we design a detail-enhanced shared convolutional scaling detection
head (DESCS-DH) to enable the model to effectively capture edge information of
diseases and address issues such as poor performance in object detection across
different scales. Finally, we employ the NWD loss function to replace the CIoU
loss function, allowing the model to locate and identify small targets more
accurately and further enhance its robustness, thereby facilitating rapid and
precise identification of apple leaf diseases. Experimental results demonstrate
ELM-YOLOv8n’s effectiveness, achieving 94.0% of F1 value and 96.7% of mAP50
value—a significant improvement over YOLOv8n. Furthermore, the parameter
count and computational load are reduced by 44.8% and 39.5%, respectively. The
ELM-YOLOv8n model is better suited for deployment on mobile devices while
maintaining high accuracy.
KEYWORDS
orchard environments, disease detection, deep learning, ELM-YOLOv8n, DESCS-DH
Frontiers in Plant Science frontiersin.org01
OPEN ACCESS
EDITED BY
Pei Wang,
Southwest University, China
REVIEWED BY
Jiye Zheng,
SAAS, China
Felix Olanrewaju Babalola,
Final International University, Cyprus
*CORRESPONDENCE
Wenjie Sang
swj@sdust.edu.cn
RECEIVED 16 December 2024
ACCEPTED 12 February 2025
PUBLISHED 13 March 2025
CITATION
Wang G, Sang W, Xu F, Gao Y, Han Y and
Liu Q (2025) An enhanced lightweight
model for apple leaf disease detection
in complex orchard environments.
Front. Plant Sci. 16:1545875.
doi: 10.3389/fpls.2025.1545875
COPYRIGHT
© 2025 Wang, Sang, Xu, Gao, Han and Liu. This
is an open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Original Research
PUBLISHED 13 March 2025
DOI 10.3389/fpls.2025.1545875

1 Introduction
As one of the most widely cultivated fruits globally, the annual
production of apples significantly influences the agricultural
economy and the global food supply chain (Wani and Mishra,
2022). As the largest producer of apples worldwide, China
contributes 40.5 million tons annually, representing 46.7% of the
global production (Liu et al., 2024b). However, the yield and quality
of apples are affected by a variety of diseases, such as Rust, Grey
Spot, Powdery Mildew, and Scab, which not only reduce the quality
of fruits, but also directly lead to a decrease in yield. Therefore,
accurate and timely identification and detection of leaf diseases is
crucial during apple cultivation. This can assist farmers in more
effectively controlling the spread of diseases, thereby maintaining
apple quality and production levels, and ultimately achieving both
economic and environmental benefits. Traditional identification of
crop diseases primarily depends on the expertise of agricultural
specialists and visual assessment. This method is inefficient and
limited by the expert’s knowledge and experience, which is prone to
misjudgment and omission. With the development of computer
technology, machine learning has started to find application in the
recognition of crop diseases (Ramesh et al., 2018). However,
traditional feature extraction algorithms usually need to rely on
manual design and domain expertise to select and construct
features. Compared to traditional machine learning methods,
deep learning methods can automatically learn and extract
features, reducing the workload of manual feature engineering
and exhibiting better performance when dealing with large-scale
data. In 2020, Li et al. (2020) introduced a finely-tuned GoogLeNet
model, which was fine-tuned to improve over the state-of-the-art
method by 6.22%.In 2021, Soujanya and Jabez (2021) proposed a
technique based on improved AlexNet that can successfully classify
38 different classes of healthy and diseased plants, and the best
accuracy of this method is 96.5%.In 2022, Wei et al. (2022)
proposed a multi-scale feature fusion based network model for
accurate identification and classification of crop pests and diseases,
the classification accuracy reached 98.2%.In 2023, Islam et al. (2023)
used ResNet50 migration learning model as the core for
distinguishing between healthy and infected leaves and classifying
current disease types, providing an even higher accuracy of 98.98%.
In 2024, Faisal et al. (2024) proposed a method based on deep CNN
architecture to recognize and classify cotton weeds efficiently and
the proposed model achieved 98.3% accuracy which is better than
other models. Although these studies have achieved satisfactory
accuracy rates when dealing with the task of classifying a single disease
image with a simple background, however, the performance may still
be insufficient in real-world applications when faced with more
complex and varied scenarios, as well as in mobile deployments.
Faced with real-time detection in complex environments, object
detection algorithms (Ren et al., 2016;Liu et al., 2016;Redmon,
2016) show greater advantages. Sun et al. (2021) proposed a
lightweight CNN model, MEAN-SSD, which can be deployed on
mobile devices for real-time detection of apple leaf diseases. Khan
et al. (2022) proposed a two-stage real-time detection system for
apple leaf diseases based on Xception and Faster-RCNN-based two-
stage real-time apple disease detection system, which achieved an
overall classification accuracy of 88%. Zhang et al. (2023b) designed
BCTNet network for accurate apple leaf disease detection, which
solved the problems of unconstrained environmental factor
interference and low detection accuracy caused by significant
changes in the target scale of apple leaf disease detection. Lv and
Su (2024) proposed YOLOV5-CBAM-C3TR for apple leaf disease
detection, which showed strong recognition ability in identifying
similar diseases, and is expected to promote the further
development of disease detection technology. Chang and Lai
(2024) integrated a lightweight convolutional neural network
architecture, RegNetY-400MF, with a transfer learning technique,
which not only improves the accuracy of potato leaf disease
detection, but also reduces the computational and storage
requirements. Liu et al. (2024a) proposed MCDCNet to extract
more reliable apple leaf disease features with various scales and
geometries, which effectively improved the discriminative ability of
the network. However, the reduction of model parameters and
computational load may lead to the decrease of accuracy, how to
strike a balance between lightness and accuracy is a hot research
topic nowadays (Yan et al., 2024).
In summary, although the crop disease recognition method based
on Convolutional Neural Networks (CNN) solves the problem of
inefficiency of traditional machine vision recognition, there are still
problems such as high model complexity and low accuracy of small
target disease recognition under complex background. To address
these challenges, this study introduces a lightweight detection model
for identifying apple leaf diseases against complex backgrounds. First,
the Fasternet module is integrated to decrease the number of
parameters and computational load of the model; second, an
Efficient Multi-Scale Attention (EMA) is incorporated to improve
the featureextraction ability ofthe networkfor small target diseases as
well as the differentiation ability for similar diseases; then, the DESCS-
DH detection head is designed to optimize the model’s effectiveness in
the face of multi-scale target detection. Finally, the NWD loss function
is employed to improve the model’s precision in localizing and
identifying small targets, thereby enhancing the model’s robustness
and facilitating rapid and accurate identification of apple leaf diseases.
2 Related works
With the development of artificial intelligence technology,
machine learning and deep learning have been widely used in the
field of automatic crop disease detection. The following is a brief
overview of these two methods.
2.1 Machine learning methods
This approach first requires the extraction of disease features
from collected plant images, which are subsequently used to train
machine learning models that typically require more domain
knowledge and human intervention to select and optimize
features. In 2022, Harakannanavar et al. (2022) developed an
algorithm based on machine learning and image processing to
automatically detect tomato leaf disease. SVM, KNN, and CNN
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were used to classify features, and the accuracy of the three methods
used was 88%, 97%, and 99.6%, respectively. In 2023, Gaikwad and
Musande (Gaikwad and Musande, 2023) developed an advanced
crop disease prediction technique using cetalatran-optimization
algorithm, deep KNN, and relief algorithm, the accuracy reached
91.879%. Ahmed and Yadav (2023) developed a crop disease
prediction model using back propagation ANN, SVM, GLCM,
and k-mean algorithm, the accuracy reached 99%.
2.2 Deep learning methods
Deep learning methods, such as convolutional neural networks
(CNN), can learn features directly from the original image without the
need for a complex feature extractionprocess. At present, the research
on deep learning in the field of plant disease diagnosis is developing
rapidly, which provides strong technical support for agricultural
production. Many researchers use famous CNN architectures such
as AlexNet, VGG, GoogLeNet, and ResNet for disease classification.
Anim-Ayeko et al. (2023) proposed a ResNet9 model that detects the
blight disease in both potato and tomato leaf images for farmers to
leverage, the model achieved 99.25% accuracy. Reddy et al. (2023)
proposed a customized PDICNet model for crop disease identification
and classification, with an accuracy and F1-score of 99.73% and
99.78%, respectively, for PlantVillage dataset. Current models based
on disease classification have reached a high accuracy.
To further localize wherethe disease is located, two-stage detection
algorithms and single-stage detection algorithms have been applied to
disease detection, such as Faster R-CNN, YOLO, and SSD. We chose
advanced object detection models for apple leaf disease detection and
analyzed them in comparison with our model in comprehensive
aspects. As shown in Table 1, for dataset selection, we synthesized
the two most commonly used publicly available apple disease datasets
to ensure diversity of data sources. In terms of disease types, we chose a
wider range of disease types, selecting six of the mostcommon ones. In
terms of research methodology, we not only pay attention to the
improvement of model performance, but also focus on the resource
consumption of the model. In addition to improving the traditional
featureextraction module,we also proposed a Detail-EnhancedShared
Convolutional Scaling Detection Head, so that the model can more
accurately capture the subtle features of the disease when facing multi-
scale targets. The model we developed not only achieves excellent
accuracy, with a mAP50 of 96.7%, but also optimizes the number of
model parameters and the amount of computation, effectively
alleviating the problem of resource consumption. In terms of the
adaptability of application scenarios, our model is able to cope with
complex field environments, showing good practicality and flexibility.
3 Materials and methods
3.1 Dataset construction
The experimentaldatasets were sourced from thepublicly available
datasets plant-pathology-2021-fgvc8 and AppleLeaf9.Both datasets
contain a large number of high-quality images of apple leaf diseases,
from which six leaf diseases were selected for labeling, namely Rust,
Scab, Grey Spot, Frog Eye Leaf Spot, Powdery Mildew, and Alternaria
Blotch, with more than 90% of the images collected in the orchard
environments. These six diseases are prevalent on apple leaves,
resulting in significant detrimental effects on agricultural
productivity, and they are representative in terms of symptom
presentation, covering different lesion types such as spotting,
powdering, and rusting. There are differences in the difficulty of
recognizing different diseases. For instance, rust is typically more
easily recognizable by the model because of its distinctive color and
shape feature, whereas Alternaria Blotch and Frog Eye Leaf Spot
impose greater demands on the model’s discriminatory capabilities
due to their more similar lesion characteristics.
A total of 2,203 images were labeled using an online platform,
Make Sense. The small target box is used to label Rust, Grey Spot, Frog
Eye Leaf Spot, and Alternaria Blotch. For Scab and Powdery Mildew,
which develop throughout the leaf, the entireleaf is labeled. Thedataset
was augmented through data enhancement techniques. To prevent
both the original and enhanced images from appearing simultaneously
in the training and validation sets, the original images were initially
divided into training, validation, and test sets in a ratio of about
8:1:1.Our model requires a large amount of data to capture the subtle
differences between different diseases, and this ratio ensures that the
model has enough data for training and the right amount of data for
validation and testing. Subsequently, the images and labels were
augmented using five techniques: horizontal flipping, vertical
flipping, translation, contrast adjustment, and brightness
adjustment, the new dataset obtained is named Apple-Leaf1.An
example of the enhancement methods is illustrated in Figure 1.The
numbers and sources of the images are presented in Table 2.
3.2 Methodology
3.2.1 Fasternet module
In order to design lighter and faster networks, many researchers
have generally focused on reducing the total amount of floating-
point operations (FLOPs) (Menghani, 2023;Zeng et al., 2022;
Zhong et al., 2022). However, merely reducing FLOPs does not
invariably result in the desired reduction in delay. The underlying
reason lies in the inefficiency of the actual floating-point operations
per second (FLOPS) performed. Chen et al. (2023) designed a new
convolution (PConv), which is able to significantly reduce
unnecessary computations and memory accesses while extracting
spatial features through a subtle design, thus significantly
improving the computational efficiency. Based on this, the
FasterNet family was further developed, as shown in Figure 2,
which is a new set of neural network architectures.
In this study, we integrate the Faster Block into the backbone
and neck to obtain C2f-Faster, which reduces the number of
parameters and computational load of the model. The Faster
Block is designed to fully incorporate the high efficiency of
PConv.The FLOPs of PConv, as shown in Equation 1, is 1/16 of
the conventional convolution.
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hwk2c2
p(1)
And the memory access of PConv, as shown in Equation 2,is1/
4 of the conventional convolution.
hw2cp+k2c2
p≈hw2cp(2)
The Faster Block achieves a significant reduction in
computational complexity and memory access by introducing
PConv, which applies regular convolution operations to a portion
of the input channel while leaving the remaining channels
unchanged in their original state. Although only a portion of the
input channel is computed, the preserved channel remains useful in
FIGURE 1
Data enhancement methods.
TABLE 1 Comparison of object detection models for apple leaf disease detection.
Model Research Method Data Source Disease Type Results
MEAN-SSD (Sun et al., 2021) A new apple leaf disease detection model MEAN-SSD was
constructed using MEAN block and Apple-
Inception module.
Self-built datasets
AppleDisease5
Five categories. Achieved 83.12% mAP
and 12.53 FPS
detection performance.
FPN-ISResNet-Faster RCNN
(Hou et al., 2023)
This study proposes a deep learning model called the
feature pyramid networks (FPNs) –inception squeeze-and-
excitation ResNet (ISResNet)–Faster RCNN.
FGVC8 and AI
Challenger 2018
Three categories. The mAP50 is 93.68%.
YOLO-Leaf (Li et al., 2024) Feature extraction using DSConv, enhancement of
attention mechanism using BiFormer, introduction of IF-
CIoU for improved bounding box regression.
FGVC7 and FGVC8 Categories four
and five.
The mAP50 scores
reached 93.88% and
95.69%, respectively.
YOLOv8n–GGi (Gao
et al., 2024)
GhostConv replaces the traditional Conv layer, replaces
part of the C2f structure with C3Ghost, integrates a
Global Attention Mechanism (GAM), and incorporates an
improved BiFPN.
AppleLeaf9 Four categories. The mAP50 is 86.9%,
GFLOPs is 5.5,
Parameters is 1.7M.
ELM- YOLOv8n(Ours) Design lightweight module c2f-faster, extract subtle
features using the EMA attention mechanism, propose
Detail-Enhanced Shared Convolutional Scaling Detection
Head, and improvement of loss function.
FGVC8
and AppleLeaf9
Six categories. The mAP50 is 96.7%,
GFLOPs is 4.9,
Parameters is 1.6M.
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subsequent PWConv layers, allowing feature information to flow
through all channels. This design enables the Faster Block to operate
rapidly on various hardware platforms, including mobile devices,
while maintaining high detection accuracy.
3.2.2 EMA module
In computer vision tasks, channel or spatial attention
mechanisms effectively extract local features of an object (Sun
et al., 2022;Wang et al., 2024b;Zheng et al., 2023). Ouyang et al.
(2023) proposed the Efficient Multi-Scale Attention, which pursues
a balance between computational efficiency and information
retention by adopting an innovative approach: reconstructing
some channels into batch dimensions and subdividing the
channel dimensions into multiple sub-feature groups. This
ensures a uniform distribution of spatial semantic features within
each sub-feature group, thereby optimizing feature expression and
processing without adding computational burden. As illustrated in
Figure 3, the EMA module first divides the input into multiple
grouped feature maps, which are then processed through three
parallel path branches: two perform one-dimensional global
pooling, and the third conducts feature extraction via 3x3
convolution. Ultimately, the output feature maps within each
group are aggregated by computing the two generated spatial
attention weights and then processed using a Sigmoid function to
capture pairwise pixel relationships and emphasize the global
contextual information of all pixels.
The onset sites of apple leaf diseases typically appear as small
spots on the leaves, particularly during the early stages of infection.
However, these small lesions are difficult to detect due to the
complex background. Furthermore, the initial symptoms of
several apple leaf diseases, such as Alternaria Blotch, Grey Spot,
and Frog Eye Leaf Spot, are remarkably similar, making them
challenging to distinguish. To address the issue of insufficient
feature extraction for small target disease against a complex
background, the EMA mechanism is integrated into the deep
layers of the network’s backbone, as illustrated in Figure 4.
Feature extraction is conducted in depth following Pconv and two
1×1 convolution operations, enabling the model to focus more on
disease details and thereby enhancing recognition accuracy.
3.2.3 DESCS-DH (detail-enhanced shared
convolutional scaling detection head)
Although YOLOv8 performs well on many tasks, there is still
room for improvement in the design of the detection head. The first
is the large number of parameters, the original detection head
employs one 1×1 and two 3×3 convolutions for feature extraction
before predicting the object class and positional offset within each
bounding box, which inevitably results in a considerable parameter
increase; second is that the use of a normal convolution does not
capture the detailed information of the image very well during
feature extraction, which may lead to important information loss;
and lastly, the reliance on predefined anchors may cause the model
FIGURE 2
Fasternet Block. The "*" represents Convolution.
TABLE 2 Number and source of images in the Apple-Leaf1 dataset.
Disease Name Image Source Original Quantity Enhanced
Training Set
Enhanced
Validation Set
Enhanced
Test Set
Rust 2021-fgvc8 521 2496 282 348
Scab 2021-fgvc8 345 1620 234 216
Grey Spot 2021-fgvc8 316 1560 162 174
Frog eye leaf spot 2021-fgvc8 365 1794 192 204
Powdery Mildew AppleLeaf9 348 1692 210 186
Alternaria Blotch AppleLeaf9 308 1404 240 204
Total 2203 10566 1320 1332
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to perform poorly in detecting targets of different scales and
proportions, as the anchors may not be well adapted to the
shapes and sizes of all objects. To address these challenges, we
design a detail-enhanced shared convolutional scaling detection
head named DESCS-DH.
DESCS-DH is an efficient and lightweight detection head, which
can maintain high accuracy with reduced number of parameters and
computation. As shown in Figure 5,firstly, DESCS-DH significantly
reduces the number of parameters by fusing P3, P4, and P5 feature
maps at different scales througha shared convolutional layerfor feature
interaction and parameter sharing. Secondly, it uses detail-enhanced
convolution to improve the ability of the detection head to capture
image details. DEConv significantly enhances the model’s
representation and generalization performance by incorporating a
priori information into the ordinary convolutional layers. In addition,
through the application of reparameterization techniques, DEConv
can be equivalently converted to ordinary convolution, a process that
does not require the addition of extra parameters or computational
cost, thus effectively improving performance while keeping the model
lightweight. Finally, DESCS-DH can adaptively modify its internal
parameters based on the dimensions of the input image and feature
map, so that when facing multi-scale targets, each detector head can
obtain the optimal working parameters, which enables it to maintain
stable performance in a variety of complex environments, thereby
FIGURE 3
EMA module.
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enhancing the robustness of the entire model and its
detection efficiency.
3.2.4 NWD loss function
In the field of object detection, Intersection over Union (IoU)
has long been a standard metric for measuring the accuracy of
bounding boxes (Peng and Yu, 2021;Zhang et al., 2022;Tian et al.,
2022). However, for the detection of tiny objects, the performance of
IoU is not satisfactory, which is mainly due to the small overlapping
area of the bounding box of tiny objects, resulting in a low value of IoU,
which is difficult to accurately reflect the actual detection accuracy. To
solve this problem, Wang et al. (2021) proposed a novel metric based
on the Wasserstein distance, aiming to evaluate the similarity of the
bounding boxes of tiny objects more effectively.
Specifically, the method first models the shape and position
information of the bounding box as a two dimensional Gaussian
distribution. This modeling approach captures the characteristics of
the bounding box in more detail, especially for objects with irregular
shapes or small sizes. Next, the Normalized Wasserstein Distance
(NWD) is introduced to quantify the similarity between these
Gaussian distributions. An additional significant benefitoftheNWD
is its insensitivity to object scale, enabling more precise localization for
tiny objects. In addition, the application of NWD is not limited to the
label assignment stage; it can also replace the IoU in the non-maximal
suppression (NMS) process and be used in the regression loss function,
thereby improving the performance of tiny object detection in general.
The computational procedure is as follows, after modeling the bounding
box as a two dimensional (2D) Gaussian distribution, the distribution
distance is calculated using the Wasserstein distance metric. For two
dimensional Gaussian distribution, u1 and u2 as shown in Equation 3:
m1=N(m1,S1), m2=N(m2,S2)(3)
Define the distance between them as Equation 4:
W2
2(m1,m2)= m1−m2
kk
2
2+Tr S1+S2−2(S1=2
2S1S1=2
2)1=2

(4)
The simplified formula is shown in Equation 5:
W2
2(m1,m2)= m1−m2
kk
2
2+S1=2
1−S1=2
2





2
F(5)
Furthermore, for Gaussian distributions Naand Nb, which are
derived from bounding boxes A = (cxa, cya,w
a,h
a) and B =
(cxb, cyb,w
b,h
b), Equation 5 can be simplified to Equation 6:
W2
2(Na,Nb)= cxa,cya,wa
2,ha
2

T
,cxb,cyb,wb
2:hb
2

T








2
2
(6)
However, the numerical range of W2
2(Na,Nb) is not suitable for
direct use as a similarity metric. Consequently, its exponential form
is adopted for normalization, yielding a new metric termed the
Normalized Wasserstein Distance (NWD), as shown in Equation 7:
NWD(Na,Nb) = exp −ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
W2
2(Na,Nb)
pC
!
(7)
In the detection and localization of small target diseases, the limited
pixel occupation in the image results in scarce semantic information,
making it challenging for the network to extract features that are
sufficiently effective for precise target categorization and localization.
The NWD loss function exhibits a unique sensitivity to both the size of
the target and the distances between targets. Traditional loss functions
may not fully consider these factors. For apple leaf disease detection,
accurately locating and distinguishing spots of different sizes and
locationrelationshipsiscrucialforenhancingdetectionaccuracy.
The NWD loss function facilitates the model’s focus on features
pertinent to disease detection during training, owing to its sensitivity
to target size and inter-target distances.
3.2.5 ELM-YOLOv8n network architecture
YOLOv8, an advanced object detection algorithm, effectively
strikes a balance between detection accuracy and speed (Wan et al.,
2024;Zhu et al., 2024). Consequently, in this study, we selected and
enhanced the YOLOv8n model to develop a new network
architecture termed ELM-YOLOv8n.The YOLOv8 architecture
comprises four components: Input, Backbone, Neck, and Head.
The Backbone extracts features from the input image data using
CSPDarknet53 to convert raw pixels into high-level semantic
features. The Neck serves as the middle layer of the network
structure and fuses the features of different levels through
techniques such as Feature Pyramid Network (FPN).The Head is
the output layer of the network, which predicts the information of
the target’s category, location, and confidence level based on the
fused features. As shown in Figure 6, the enhanced lightweight
model ELM-YOLOv8n is designed in this research to realize the
model to reduce the consumption of computational resources while
maintaining high detection performance. The C2f module in the
original YOLOv8 consumes a significant portion of the network’s
parameters. By substituting the Bottleneck with the Fasternet Block,
we integrate the C2f-Faster structure, which effectively diminishes
the parameter count and model complexity. The EMA attention is
incorporated into the deepest layer of the Fasternet Block in
Backbone, and after Pconv and two 1×1 convolutional operations,
the deep subtle features are extracted. The Head employs the
DESCS-DH, addressing the issues of high parameter count and
inadequate detail capture in the original detector head, as well as
inconsistencies in performance across varying target sizes. Lastly,
the NWD loss function for small target detection is utilized in place
of the CIOU loss function to enhance the model’s accuracy in
localizing small targets.
FIGURE 4
Architecture for Fasternet-EMA.
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3.3 Experimental environment
3.3.1 Experimental platform
The experiment was conducted using a 64-bit Windows 11
operating system, with the PyTorch framework, PyCharm IDE, and
Python programming language employed for model training. To
enhance data diversity and mitigate overfitting, the mosaic
augmentation technique was applied during the training phase.
The detailed experimental environment and some experimental
parameters are presented in Tables 3,4, respectively.
3.3.2 Evaluation metrics
The model is evaluated on two dimensions: performance and
complexity. Performance metrics include precision (P), which
measures the proportion of positive samples detected by the
model that are actually positive; recall (R), which refers to the
proportion of samples that are actually in the positive category that
are correctly predicted by the model to be in the positive category;
F1 score, which is the harmonic mean of precision and recall;
average precision (AP), which is a combination of precision and
recall and is obtained by plotting a precision-recall curve and
calculating its area; and mean average precision in multiple
categories(mAP), which calculates the AP for each category and
averages the values. The specific calculation formulas are shown in
Equations 8–12:
P= TP
TP + FP 100 % (8)
R= TP
TP + FN 100 % (9)
F1=2PR
P+R100 % (10)
AP = Z1
0
P(R)dR 100 % (11)
mAP = 1
No
k=N
k=1
APk100 % (12)
In complexity evaluation, three main indicators are considered: the
number of model parameters(Params), Giga Floating-point Operations
Per Second (GFLOPs), and the model size. The number of parameters
of the model is the total number of all trainable parameters in the
model, which are optimized during training to reduce prediction
errors.The parameter count serves as a crucial indicator of a model’s
complexity. GFLOPs is used to measure the amount of model
computation. Model size denotes the memory space required for
storing the model on disk and aids in evaluating its deployment
viability in environments with limited resources.
FIGURE 5
The structure of DESCS-DH. (A) DESCS-DH (B) DEConv.
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4 Results and analyses
4.1 Effect of incorporating different
attention mechanisms into the backbone
C2f on model performance
Incorporating different attention mechanisms into the deepest
layer of the Fasternet Block in the backbone network, the attention
mechanisms together with the lightweight module resulted in a
reduction in the number of parameters and computation of the
whole model. As shown in Table 5, the CF-LSKA has the highest
precision of 94.6%. In the F1 score, a composite metric, both CF-
TripleAttention and CF-EMA achieve 92.5%, indicating that they
strike a good balance between precision and recall. On the key
metrics of mAP50 and mAP50-95, CF-EMA performs well with
96.3% and 66.5%, respectively. Finally, CF-EMA shows excellent
efficiency in terms of parameter count and computation, with its
parameter count of only 2.65M and its GFLOPs remaining at 7.1,
which means that it maintains high performance with high
computational efficiency and low resource consumption.
FIGURE 6
ELM-YOLOv8n overall framework and its component modules (A) ELM-YOLOv8n. (B) Conv. (C) SPPF. (D) C2f-Faster. (E) C2f-Faster-EMA.
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4.2 Effect of different detection heads on
model performance
Among the four different detection heads, with the exception of
the Aux detection head (Wang et al., 2023), the other three detection
heads reduce the number of parameters and computational
complexity of the model. Specifically, LADH (Zhang et al., 2023a)
has the lowest computational complexity, but this is accompanied by
lower accuracy. As presented in Table 6, LADH has relatively low F1
scores and mAP50 values. The Aux detection head has the highest
accuracy. However, on the whole, the DESCS-DH has relatively high
F1 score and mAP50 value, and also has a low number of parameters
and computational complexity, thus achieving a balance between
performance and computational resources.
4.3 Performance comparison of different
loss functions
Model training and validation were performed using different
loss functions. As presented in Table 7, the NWD loss function
yields the highest precision, F1, and mAP50, with respective values
of 94.1%, 93.5%, and 96.6%. As shown in Figure 7, all five loss
functions significantly decreased the model’s boundary regression
loss throughout the network’s training process. However, the
DIOU, CIOU and EIOU resulted in relatively high loss value,
whereas the NWD loss function achieved the lowest loss value.
4.4 Ablation experiments
As shown in Table 8,theF1,mAP50,andmAP50-95of
YOLOv8n on the test set are 92.3%, 95.5%, and 66.2%,
respectively. Following the integration of Fasternet Block, the
model’s parameter count and computational complexity were
significantly reduced, accompanied by a minor decline in the F1
score and a slight enhancement in the mAP50 metric. After
incorporating the EMA mechanism, only a slight increase in
model parameters and computational effort was exchanged for a
significant improvement in F1 score and mAP50-95 value, by 1.4%
and 1.2%, respectively. The adoption of the DESCS-DH detection
head further significantly reduced the model’s complexity, yielding
a 0.1% increase in mAP50 value and a 0.2% reduction in the F1
score. With the introduction of the loss function NWD, the
parameters and computational effort of the model did not change,
but the F1, mAP50, and mAP50-95 improved by 0.9%, 0.2%, and
0.6%, respectively. Consequently, the model’s F1 score and mAP50
value were enhanced by 1.7% and 1.2%, respectively, compared to
the baseline YOLOv8n model. Additionally, the model’s parameter
count and computational effort were reduced by 44.8% and
39.5%, respectively.
Figure 8 illustrates that the ELM-YOLOv8n model exhibits
superior accuracy in identifying various apple diseases compared
to the original YOLOv8n model, with the performance
improvement being particularly pronounced for the detection of
small target diseases. For example, the mAP50 indexes for detecting
Grey Spot, Alternaria Blotch, and Frog Eye Leaf Spot, which are
TABLE 3 Experimental environment.
Environment Configuration
Operating system Windows 11
CPU AMD Ryzen 7 7735H
GPU NVIDIA GeForce RTX 4060
RAM 16GB
Programming language Python 3.8.19
Deep learning framework Pytorch 2.2.1
CUDA CUDA version12.1
TABLE 4 Experimental parameters.
Parameters Setup
Input image size 640 × 640
Batch size 16
Workers 8
Optimizer SGD
Learning rate 0.01
Epochs 100
TABLE 5 Effect of different attention mechanisms on model performance.
Attention P% R% F1% mAP50% mAP50-95% Params/M GFLOPs
CF-LSKA 94.6 90.0 92.2 96.1 66.3 2.68 7.1
CF-CAFM 92.4 91.9 92.1 95.8 66.2 2.86 8.1
CF-SEAttention 93.1 91.3 92.2 95.8 65.3 2.65 7.0
CF-TripleAttention 94.5 90.5 92.5 96.2 66.4 2.65 7.0
CF-CAA 93.0 91.3 92.1 95.9 65.7 2.71 7.2
CF-MLCA 92.5 91.2 91.9 95.9 66.0 2.65 7.0
CF-EMA 94.0 91.0 92.5 96.3 66.5 2.65 7.1
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small target diseases, were improved by 2.0%, 2.2%, and
1.0%, respectively.
4.5 Comparison with other models
Table 9 presents the performance comparison of the seven
object detection algorithms on the test set. ELM-YOLOv8n achieves
the highest F1 and mAP50 scores of 94.0% and 96.7%, respectively,
where mAP50 compares favorably with Faster R-CNN, RT-DETR-l,
YOLOv5n, YOLOv7-tiny, YOLOv8-Mobilenetv4, and YOLOv10n
by 24.2%, 1.8%, 2.1%, 0.9%, 3.3%, and 0.2%, respectively. Figure 9
depicts the changes in mAP50 for various models throughout the
training process. With respect to model complexity, ELM-
YOLOv8n has the fewest parameters, totaling only 1.66M.
Regarding computational and model size, as illustrated in
Figure 10, Faster R-CNN exhibits the highest computation and
largest model size, at 948.2 GFLOPs and 108.0 MB, respectively. In
contrast, YOLOv5n has the smallest computation and model size,
followed by the improved model, but the improved model’s F1,
mAP50, and mAP50-95 values are 2.5%, 2.1%, and 3.1% higher
than YOLOv5n, respectively.
TABLE 7 Effect of different loss functions on model performance.
Loss Function P% R% F1% mAP50% mAP50-95% Params/M GFLOPs
DIOU 92.4 92.2 92.3 95.8 66.3 3.01 8.1
CIOU 92.9 91.6 92.2 95.8 66.5 3.01 8.1
EIOU 93.5 91.2 92.3 95.8 67.0 3.01 8.1
WIOU 92.8 91.8 92.3 96.0 66.8 3.01 8.1
NWD 94.1 93.0 93.5 96.6 66.9 3.01 8.1
TABLE 6 Effect of different detection heads on model performance.
Detection Head P% R% F1% mAP50% mAP50-95% Params/M GFLOPs
LADH 92.4 90.9 91.6 95.3 66.3 2.36 5.7
Auxhead 94.0 89.6 91.7 95.2 66.3 3.76 11.2
SEAMHead 93.1 92.0 92.5 96.0 66.5 2.82 7.0
DESCS-DH 93.7 91.3 92.5 96.3 66.6 2.36 6.5
FIGURE 7
Change in loss value.
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4.6 Generalization experiment
To evaluate the model’s adaptability to new data and its
performance in various environments and scenarios, we
conducted generalization experiment. A collection of 823 apple
disease images from different scenarios were selected to be collected
under different light, weather and geographic locations, with 755
images from field scenarios and 68 images from simple scenarios to
simulate a variety of situations in the real world, thus testing the
stability of the model under these conditions. Images were selected
to cover a wide range of disease types including Rust, Scab, Grey
Spot, Frog Eye Leaf Spot, Powdery Mildew, and Alternaria Blotch,
naming this dataset Apple-leaf2.The example of the dataset is
illustrated in Figure 11.
Experimental results show that the model can work efficiently in
simplescenariosaswellasremainrobustincomplexscenarios.As
shown in Table 10, the ELM-YOLOv8n model exhibits an enhanced F1
score of 0.8% and an mAP50-95 value improvement of 1.1%, relative to
the YOLOv8n. Concurrently, the parameter count and computational
complexity are diminished by 44.8% and 39.5%, respectively. These
results suggest that the improved model has strong detection
capabilities for leaf diseases in multifactorial complex scenarios and
is more suited for deployment on mobile devices.
To intuitively illustrate the interpretability of the model’s
decisions, we utilize XGrad-CAM to visualize the model’s
predictions. This method involves computing the gradient of the
feature map in the convolutional neural network relative to the
target category, combining these gradients with the feature map
TABLE 8 Results of ablation experiments.
Fasternet EMA DESCS- DH NWD P% R% F1% mAP50% mAP50-95% Params/M GFLOPs
× × × × 93.2 91.3 92.3 95.5 66.2 3.01 8.1
✓× × × 93.7 90.2 91.9 95.9 65.3 2.30 6.3
✓✓ × × 94.5 92.2 93.3 96.4 66.5 2.31 6.4
✓✓ ✓ × 93.8 92.5 93.1 96.5 66.3 1.66 4.9
✓✓ ✓ ✓94.5 93.6 94.0 96.7 66.9 1.66 4.9
The “✓”indicates that the model employs this module, while the “x”indicates that the model does not employ this module.
FIGURE 8
MAP50 values of YOLOv8n and ELM-YOLOv8n in the detection of six diseases.
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values to obtain importance scores, and normalizing the result to
create a heat map that highlights the critical regions in the image for
decision-making. Figure 12 displays the heat maps comparison
between YOLOv8n and ELM-YOLOv8n on selected test sets. The
heat maps generated by ELM-YOLOv8n reveal that the predicted
targets exhibit darker colors, signifying that the model allocates
greater attention to regions of interest within the target image
during the prediction.
5 Discussion
Current apple disease detection algorithms confront various
challenges, including large model parameters, high computational
complexity, and long inference times (Ma et al., 2024). To effectively
address these issues and achieve accurate detection of apple leaf
diseases, this study introduces the Fasternet Block and proposes a
detail-enhanced shared convolutional scaling detection head
(DESCS-DH) for constructing a lightweight, real-time object
detection model. Incorporating the Fasternet Block into C2f
reduced the model’s parameters and computational requirements
by 23.5% and 22.2%, respectively. The F1 score slightly decreased by
0.4%, while the mAP50 increased by 0.4%. This can be attributed to
PConv in Fasternet, which convolves only a subset of input channels,
significantly reducing model complexity and storage space needs.
This allows the Fasternet Block to effectively manage the number of
model parameters with minimal performance degradation. Utilizing
DESCS-DH as the detection head for YOLOV8, the model’smAP50
is enhanced by 0.8% while simultaneously decreasing model
complexity. DESCS-DH is an efficient and lightweight detection
head that boasts several advantages, including the use of shared
convolutions for parameter sharing, which drastically decreases the
number of parameters. In the task of apple leaf disease detection,
critical feature information of the target, such as disease edges and
textures, is often found in image details. Standard convolution
operations may overlook these subtle yet vital details; however,
TABLE 9 Performance comparison of different network models on test sets.
Model P% R% F1% mAP50% mAP50- 95% Params/M GFLOPs
Faster R-CNN 70.2 74.5 72.3 72.5 42.1 28.33 948.2
RT-DETR-l 91.7 91.8 91.7 94.9 65.0 32.0 103.5
YOLOv5n 91.9 91.1 91.5 94.6 63.8 1.77 4.2
YOLOv7-tiny 92.8 91.7 92.2 95.8 65.2 6.02 13.2
YOLOv8-Mobilenetv4 92.1 90.0 91.0 93.4 63.3 5.70 22.5
YOLOv10n 93.5 93.0 93.2 96.5 67.7 2.27 6.5
ELM-YOLOv8n 94.5 93.6 94.0 96.7 66.9 1.66 4.9
FIGURE 9
Changes in mAP50 during training of different models.
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DEConv within DESCS-DH effectively captures them. When
combined with re-parameterization techniques, DEConv optimizes
the convolution operation without increasing the model’s
computational load, thereby enhancing the model’sresponsiveness
to input data. In real-world disease detection scenarios, the target
sizes vary significantly. DESCS-DH can adaptively modify its internal
parameters, ensuring that the detection head maintains stable
performance across a range of target sizes.
In the recognition of small target diseases on apple leaves within
complex backgrounds, the primary challenges are the small size of
target objects, their subtle features, and their susceptibility to
interference from background noise, lighting variations, leaf
FIGURE 10
Comparison of computation and model size for different models.
FIGURE 11
Example of Apple-leaf2 dataset.
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occlusion, and similar textures. These challenges compound the
difficulty of detection, thereby diminishing recognition accuracy
(Shao et al., 2024;Wang et al., 2024a). Incorporating the EMA
mechanism into YOLOv8n enhances the mAP50 by 0.8%. The
EMA mechanism constructs local cross-channel interactions within
each parallel sub-network without reducing channel dimensions.
Through a cross-space learning approach, it fuses the output feature
maps of the two parallel sub-networks and generates better pixel-
level attention for high-level feature maps. Thus, the model can
focus on the disease area and reduce the influence of disturbing
factors, even in the presence of light variations or background noise.
Spatial attention weights, processed by the sigmoid function,
capture pairwise pixel relationships, facilitating the model’s
comprehension of structural information within the diseased area
and enabling the recognition of minute disease features through
pixel interactions, even when such features are not readily apparent.
In the detection and localization of small target lesions, they occupy
fewer pixels in the image, which makes it difficult for the model to
accurately determine the category and location of the target, while
the NWD (Normalized Wasserstein Distance) loss function is
TABLE 10 YOLOv8n and ELM-YOLOv8n detection results on Apple-leaf2 dataset.
Dataset Model P% R% F1% mAP50% mAP50- 95% Params/M GFLOPs
Apple-leaf2 YOLOv8n 85.5 94.5 89.8 94.8 57.8 3.01 8.1
ELM-YOLOv8n 85.6 96.3 90.6 95.6 58.9 1.66 4.9
FIGURE 12
Comparison of YOLOv8n and ELM-YOLOv8n heat maps on selected test sets.
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sensitive to the size and distance of the target. Utilizing the NWD as
a loss function for YOLOV8, the model’s F1 score is enhanced by
1.2%, and the mAP50 value is incremented by 1.1%.
Apple orchards usually have a large area, and traditional
artificial disease detection methods are not only extremely
inefficient, but also difficult to achieve comprehensive and timely
monitoring. The enhanced lightweight model proposed in this
study has significant features such as high precision, low
computational complexity and small model size, which makes it
very suitable for deployment on mobile devices with limited
resources. In the next research, we plan to deploy the model on
the UAV. By integrating edge computing devices on the UAV, the
model can process the collected image data in real time during UAV
flights, timely and accurately identify diseases in the orchard, and
provide support for the disease prevention and control of the
orchard. The UAV can automatically fly according to the preset
route and complete the scanning of the entire orchard in a short
time, which greatly improves the detection efficiency.
The ideal UAV platform should have the following
characteristics: sufficient computing power(GPU over 2GB of
VRAM), a high-resolution camera, equipped with a Global
Positioning System (GPS), automatic flight control, wireless
communication systems, multiple sensors, and other functional
components, preferably a multi-rotor UAV (Chen et al., 2021).
However, the accuracy of the model is affected by the distance of the
drone’s image capture and the flight speed. Based on theoretical
analysis, in order to ensure a certain field of view and obtain a clear
image of the leaf, the appropriate flight altitude should be
maintained 3-5 meters (Hou et al., 2023), which can meet the
needs of most disease detection. With the improvement of camera
resolution, the height can be increased to more than 5 meters. The
appropriate flight speed should be set 1.5-3 m/s. By adjusting the
shooting parameters, most common diseases can be accurately
recognized. As the flight speed increases, the recognition accuracy
of the model will decline, and there may be a small amount of
missed detection for some extremely subtle disease features, such as
the early mild symptoms of apple powdery mildew. In addition, the
present model has some limitations in dealing with the presence of
multiple diseases in a leaf, because the training data primarily target
the case of a single disease. Therefore, the model still requires
further optimization and improvement for the identification of
multiple diseases. We will work on these challenges in our future
work to enhance the comprehensive performance and application
scope of the model.
6 Conclusions
To achieve real-time detection of apple leaf diseases in complex
environments, this study introduces an enhanced lightweight
model, ELM-YOLOv8n.In order to overcome the challenge of
inadequate feature extraction from small targets in complex
backgrounds, we incorporate the Efficient Multi-Scale Attention
(EMA), which captures both channel and spatial information,
enhancing feature representation without a significant increase in
parameters or computational cost. To decrease the parameter count
and computational load, the Fasternet Block is integrated into the
C2f module of the backbone and neck, substantially reducing the
model’s complexity. In order to make the network more lightweight
and better extract the edge information of the disease, we design the
Detail Enhanced Shared Convolutional Scaling Detection Head
(DESCS-DH), which can maintain the high accuracy with fewer
parameters and computation. The detection head can adaptively
modify its internal parameters in response to the input feature map
size, ensuring optimal operating parameters for multi-scale targets,
thereby enhancing resilience to environmental changes and
interference. Lastly, utilizing the NWD loss function enhances the
model’s precision in localizing and identifying small targets, which
further enhances the model’s recognition accuracy.
The experimental results indicate that the ELM-YOLOv8n
algorithm performs well in terms of computational resource
consumption while maintaining excellent detection accuracy,
which fully meets the demand for real-time processing.
Compared to other current models, this approach not only
significantly improves the detection accuracy, but also drastically
reduces the demand for computing platform resources, making it
more feasible to be deployed on resource-constrained devices.
Future research will concentrate on enhancing the model’s
robustness across various environmental conditions and on
optimizing its capability to detect and process a broader range of
crop diseases. The optimized model is poised for successful
application in resource-limited embedded detection systems, with
the algorithm set to undergo further refinement to guarantee its
efficiency and reliability in practical settings.
Data availability statement
The original contributions presented in the study are included
in the article/supplementary material. Further inquiries can be
directed to the corresponding author.
Author contributions
GW: Conceptualization, Funding acquisition, Supervision,
Writing –original draft, Writing –review & editing. WS:
Conceptualization, Data curation, Investigation, Methodology,
Software, Writing –original draft, Writing –review & editing.
FX: Methodology, Validation, Writing –review & editing. YG:
Software, Validation, Writing –review & editing. YH: Data
curation, Validation, Writing –review & editing. QL: Funding
acquisition, Writing –review & editing.
Funding
The author(s) declare that financial support was received for the
research and/or publication of this article. This work was supported
by the Shandong Province Science and Technology-based Small and
Medium-sized Enterprises Innovation Capacity Enhancement
Project[2024TSGC0158].
Wang et al. 10.3389/fpls.2025.1545875
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Acknowledgments
The authors are very grateful to the editor and reviewers for
their valuable comments and suggestions to improve the paper.
Conflict of interest
QL was employed by Shandong Xinhua'an Information
Technology Co., Ltd.
The remaining authors declare that the research was conducted
in the absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the
creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
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