=Paper= {{Paper |id=Vol-3944/paper3 |storemode=property |title=Lightweight segmentation of UAV images for early detection of maize leaf diseases |pdfUrl=https://ceur-ws.org/Vol-3944/paper3.pdf |volume=Vol-3944 |authors=Arnaud S. R. M. Ahouandjinou,Ambroise D. K. Houedjissin,Manhougbé Probus A. F. Kiki,François Xavier Amétépé,Kokou M. Assogba }} ==Lightweight segmentation of UAV images for early detection of maize leaf diseases== https://ceur-ws.org/Vol-3944/paper3.pdf

Lightweight segmentation of UAV images for early
detection of maize leaf diseases.
Arnaud S. R. M. Ahouandjinou2†, Ambroise D. K. Houedjissin1*†, Manhougbé Probus A. F.
KIKI1†, François Xavier Amétépé2†, and Kokou M. Assogba1†
1
Doctoral School of Engineering Science, University of Abomey-Calavi, Abomey-Calavi, Benin
2
Institute of Training and Research in Computer Science, University of Abomey-Calavi, Abomey-Calavi, Benin

Abstract
Unmanned Aerial Vehicles (UAVs) equiped with RGB cameras have emerged as effective tools for
monitoring agricultural crops. However, motion blur in UAV images can affect the accuracy of subsequent
image analysis tasks, such as disease detection in plant leaves. This study proposes a real-time image
segmentation approach for analyzing UAV-captured maize leaf images. The algorithm evaluates image blur
using the Laplacian variance, applies an adaptive Wiener filter for deblurring, segments maize leaves from
the background using color transformations, and identifies diseased regions through Canny edge and
contour detection. Experimental results demonstrate the lightweight and effectiveness of proposed
approach with less than 1s runtime, improving image quality and allowing accurate disease detection of
maize crops for real-time purpose.

Keywords
UAV, RGB images, motion blur, maize diseases detection, segmentation.

1. Introduction
Plant disease detection is a key application of UAVs and has been extensively researched [1]. One of
the advantages of using UAVs is its ability to detect diseases early and prevent their spread, thereby
reducing crop losses [2]. Decision-support systems that incorporate UAVs can lead to better decision-
making, increased production, improved product quality, and labor savings [3]. UAVs are utilized
across various crop types and for detecting multiple diseases. Some diseases present visible
symptoms, while others require temperature measurements for detection [4]. Early detection of pests
and crop diseases provides farmers and other stakeholders with enough time to prevent potential
epidemics and minimize yield losses. However, motion blur in UAV images generally caused by the
camera movement during image capture, the combined effects of atmospheric turbulence, the
shaking of the UAV platforms, high altitude or operation errors can affect the accuracy of subsequent
image analysis tasks, such as disease detection in crop plant leaves [5]. This represents a common
issue in UAV imagery and various methods have been proposed to address motion blur.
On the other hand, recent advancements in deep learning (DL) have produced various methods
for detecting and classifying plant diseases using images of infected plants [6]. However, they require
huge datasets for advanced approaches such as CNN to produce good results and large image datasets
result in increased accuracy rates [7].

Procedings of the DAAfrica’2024 workshop
*Ambroise HOUEDJISSIN
†
These authors contributed equally.
arnaud.ahouandjinou@imsp-uac.org (A. S. R. M. Ahouandjinou); abhouedjissin@gmail.com(A. D. K. Houedjissin);
kprobus2005@gmail.com (M. P. A. F. Kiki); adote007@gmail.com (F. X. Amétépé); mkokouassogba@gmail.com (K. M.
Assogba)
0000-0001-7217-5588 (A. S. R. M. Ahouandjinou); 0009-0003-1880-7283 (A. D. K. Houedjissin); 0009-0009-0070-8707
(M. P. A. F. Kiki); 0000-0002-5275-8150 (F. X. Amétépé); 0009-0004-6328-0799 (K. M. Assogba)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

CEUR
Workshop
ceur-ws.org
ISSN 1613-0073
13
Proceedings
This study presents a real-time algorithm for the segmentation of UAV images, specifically
targeting the detection of maize plant leaf diseases. The proposed method leverages motion blur
detection, adaptive Wiener filtering, color conversion combined with morphological operations,
Canny edge detection, Otsu color thresholding and contour area detection so that to isolate infected
regions. The results demonstrate the algorithm's efficacy in identifying unhealthy plant areas in less
than 1 second runtime, thereby providing a robust tool for precision agriculture in real-time. The
rest of the paper is organized as follows: in section 2, we present the related works, section 3 outlines
the proposed approach, the experimentation is introduced in section 4 and results and discussion are
presented in section 5. Finally, section 6 provides a conclusion.

2. Related works

Successful disease estimation has been demonstrated in many UAV-based imagery applications such
as [8], [9], [10]. These studies often used either the mean value of the vegetation index or the count
of pixels below a certain threshold within a plot to estimate the disease score.
Table 1 gives a synthetic comparative analysis of classical image classification techniques in plant
leaves healthy and unhealthy area detection.

Table 1
Comparison of classical image classification techniques
Classification
Techniques Description Advantages Limits References
Color Compares the distribution of - Simple to implement - Limited discriminative [1]
thresholding colors in an image using a - Fast computation power
threshold. - Sensitive to changes in
lighting conditions
Texture Analyzing textural patterns in - Captures subtle - Parameter tuning [2]
Analysis an image to characterize differences in texture required
healthy and unhealthy areas. - Robust against - May be
changes in lighting and computationally
color intensive
Machine Utilizing machine learning - High accuracy and - Requires large amounts [3]
Learning algorithms (e.g., SVM, robustness of labeled data
Models Random Forest, CNN) to learn - Can automatically - Training and inference
features and classify healthy learn complex patterns can be computationally
and unhealthy areas. costly

According to Table 1 the choice of classification algorithm depends on various factors, including the
desired level of accuracy, computational resources, and the availability of labeled data. Color
thresholding is simple and efficient but may lack the discriminative power of more complex methods
like texture analysis and machine learning models. Texture analysis can capture subtle differences
in texture but may require more computational resources. Machine learning models offer high
accuracy but come with higher complexity and resource requirements, especially during training.

3. Proposed Approach

Figure 1 describes the proposed approach.

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Figure 1: Scheme of the proposed method.
The proposed algorithm involves several key steps and each step is based on specific mathematical
operations and image processing techniques to ensure accurate segmentation and detection.

3.1. UAV Image Acquisition from field

First, UAVs equipped with high-resolution RGB cameras capture images of maize fields. The UAV
images were collected between 4:45 p.m. and 6:00 p.m., on July 14, 2024, whereas it was sunny and
windless. The DJI Mini 3 Pro, a quadcopter UAV system, was used to collect aerial images of maize
leaves from a field. This system carried an automated RGB sensor (Quad Bayer CMOS camera) which
was developed for agricultural applications.

3.1.1. Image Preprocessing

The second step in the algorithm involves preprocessing the UAV-captured images to enhance their
quality and reduce noise. This is crucial for improving the accuracy of subsequent image analysis
steps.

1. Motion Blur Detection and Assessing: the Laplacian variance [14] is used to detect motion
blur. When detected, an adaptive Wiener filter is applied to deblur the images. To assess the
degree of motion blur in the UAV images, we calculate the Laplacian variance of the
grayscale image:

𝐿𝑎𝑝𝑙𝑎𝑐𝑖𝑎𝑛(𝐼) = ∑ , + (1)

𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 = ∑ , (𝐿𝑎𝑝𝑙𝑎𝑐𝑖𝑎𝑛(𝑖, 𝑗) − 𝜇) (2)
where I is the grayscale image, μ is the mean of the Laplacian image, and N is the total number of
pixels.
The variance provides a measure of image sharpness, with lower values indicating higher blur
levels.

2. Image Deblurring by Adaptive Wiener Filter: if the image is found to be blurred, an adaptive
Wiener filter is applied to reduce noise and enhance details. The Wiener filter operates as
follows:
 Normalize grayscale image:

𝐼 = .
(3)

15
 Create averaging kernel:

𝐾= 1 × (4)

 Compute local mean and variance:

𝜇 = 𝑐𝑜𝑛𝑣𝑜𝑙𝑣𝑒2𝑑(𝐼 , 𝐾, ’𝑠𝑎𝑚𝑒’) (5)

 Compute overall variance:

𝜎 = 𝑐𝑜𝑛𝑣𝑜𝑙𝑣𝑒2𝑑(𝐼 , 𝐾, ’𝑠𝑎𝑚𝑒’) − 𝜇 (6)

 Apply Wiener filter

𝐼 = (𝐼 −𝜇 )⋅( )+𝜇 (7)

 Denormalize:

𝐼 =𝐼 ⋅ 255 (8)

3.1.2. Image Segmentation

The third step in the algorithm relates to segmentation which plays a crucial role in identifying
regions of interest within UAV images, such as healthy and infected areas of maize plant leaves, for
further analysis.

1. HSV Color Segmentation: the deblurred image is then converted to the HSV (Hue, Saturation,
Value) color space to facilitate segmentation based on color characteristics [15]. The green
color range corresponding to healthy maize leaves is defined in the HSV space, and a binary
mask is created to isolate these regions:

1 𝑖𝑓 𝑟𝑎𝑛𝑔𝑒1 ≤ 𝐻𝑆𝑉(𝑥, 𝑦) ≤ 𝑟𝑎𝑛𝑔𝑒2
𝑀𝑎𝑠𝑘 = (9)
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
where 𝑟𝑎𝑛𝑔𝑒1 and 𝑟𝑎𝑛𝑔𝑒2 define the HSV range for green color.
Morphological operations, including opening and closing, are applied to the binary
mask to remove noise and smooth the segmented regions.
𝑀𝑜𝑟𝑝ℎ𝑜𝑙𝑜𝑔𝑦(𝐼) = (𝐼 ⊙ 𝐾) ⊕ 𝐾 (10)
where ⊙ denotes morphological opening and ⊕ denotes closing, and 𝐾 is the structuring
element.

2. Division into Patch: the preprocessed image is divided into a specified number of equal-sized
patches for localized analysis of disease symptoms. The number of patches here is 10.

Calculate patch dimensions:
ℎ = (11) ; 𝑤 = (12)
where 𝐻 is the height and 𝑊 is the width of the patch

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3. Cany Edge Detection: each patch undergoes further analysis to detect and classify objects of
interest. The edges of potential diseased regions are detected using the Canny edge detection
algorithm [16], which identifies boundaries based on gradients in the image:

1 𝑖𝑓 𝐺(𝑥, 𝑦) > 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑
𝐸𝑑𝑔𝑒𝑠(𝑥, 𝑦) = (13)
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
where G(x,y) is the gradient magnitude at pixel (x,y).

3.1.3. Image Classification

The detected edges are used to identify contours, which are then classified as healthy or unhealthy
based on their area. A threshold is applied to differentiate between large healthy regions and smaller
unhealthy spots. Contours are then extracted and classified based on their area, with larger areas
typically indicating healthy regions and smaller areas potentially indicating diseased regions.
Following equations explain the classification process:
𝐴𝑟𝑒𝑎 = ∑ ∑ 𝐼 (14)
where 𝐼 is the pixel value within a contour.

𝐻𝑒𝑎𝑙𝑡ℎ𝑦 𝑖𝑓 𝐴𝑟𝑒𝑎 > 𝐴
𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 = (15)
𝑈𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
where 𝐴 is fixed here to 500.

Below is the detailed algorithm:
Algorithm 1: Lightweight Segmentation for maize leave disease detection
Input: x,y, I(x, y), ctg(), clv(), avf(), rhsv(), sgr(), mo(), dpp(), ced(), fdc() --image of leaves
Begin
1: Initialize Image Processing
Output: Result -- Segmented and classified plant regions
2: I(x, y) ← ctg(I(x, y))) -- Convert the UAV image to a grayscale image
3: σ² ← clv(I(x, y)) -- Calculate the Laplacian variance to assess image sharpness
4: if σ² < Threshold then
5: I(x, y) ← avf(I(x, y)) -- Apply the adaptive Wiener filter if Variance < threshold
6: end if
7: IHSV ← rhsv(I(x, y)) --Resize the image and convert it to the HSV color space
8: ILeaves ← sgr(IHSV) -- Segment green plant regions using predefined HSV ranges
9: Imo ←mo(IMorph--Apply morphological operations to enhance segmentation
10: Patch [ ] ← dpp(Imo) -- Divide the processed image into smaller patches
11: for each patch in Patch do
12: Edge [ ] ← ced(patch) -- Canny edge detection of each patch
13: Cntrs [ ] ← fdc(Edge [ ]) -- Classify detected edges based on health status
14: end For

4. Experimentation
The characteristics of the UAV employed for the flight mission and the computer used for testing
resulting aerial images are shown in Table 2 below.

4.1. Characteristics of UAV used for data acquisition
The mini-sized, mega-capable DJI Mini 3 Pro is just as powerful as it is portable. Weighing less than
249 g and with upgraded safety features, it is not only regulation-friendly but also the safest in its
series [17]. With a 1/1.3-inch sensor and top-tier features, it redefines what it means to fly Mini.
Table 2 shows the specifications of the small UAV used for acquiring the images of the maize
plants leaves used for constructing the dataset [18].
17
Table 2
Characteristics of the DJI Mini 3 Pro used for experimentation
Characteristics Specifications
Model DJI Mini 3 Pro
Weight Under 250g
Camera 1/1.3" (0.77") 48MP f1.7 Quad Bayer CMOS Sensor
Frame per second (fps) 4K 60 fps with HDR; 1080p 120fps
Camera Orientation Horizontal and Fully Vertical camera orientations
Sensors Obstacle Avoidance Sensors
Flight Mode Intelligent
Flight times Up to 47 minutes (with the optional larger batter) or 25-
30 minutes with the standard supplied batteries.
Controller New Smart with 1080p 30fps
Maximum flight speed 16 m/s
Maximum flight height 4000m
Maximum horizontal range 8KM

4.2. Experimentation site localization
The images were accessed on 14 July 2024 and acquired on board the UAV, in the village of Dodji-
Sèhè inside Sekou in the town of Allada, Benin Republic. Following Figure 2 and Figure 3 give
additional details on the site of study.

Figure 2 : Acquisition site localization, Figure 3 : Dodji-Sèhè village, Sékou District.
Benin Republic Map.

4.3. Dataset
The dataset consists of 266 images. When the images are captured in the state of stabilization of the
device they are usually clear. On the other hand, the images captured during flight time are subject
to motion blur.
The selected images were in the JPG file format and 4032 x 3024 pixels (see Figure 4).

(a) (b) (c) (d)

(e) (f) (g) (h)
Figure 4 : Example of UAV images of maize leaves from the dataset. Image from (a) to (d) appear to
be sharp whereas those from (e) to (h) are motion-blurred.
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5. Results and discussion
5.1. Preprocessing steps
Below we present a sample of image from the dataset with preprocessing steps as follows:

(a) (b) (c)

Figure 5: (a) Original image, (b) Adaptive Wiener deblurred image of (a), (c) histogram of b
Table 3 indicates the performances characteristics of the deblurred image.
Table 3
Performances of the Adaptive Wiener Filter image deblurring step
Image Quality Index Value
Image Entropy 6.508706806116296
MSE 704.7281638886699
PSNR 19.65058732797596
SSIM 0.853058838351285
This deblurred image is characterized by an Entropy of 6,50; a Minimun Square Error of 704,72; a
Peak Signal to Noise Ratio of 19,65 and a Structural Similarity Index Measure of 0,85, which indicates
generation of an image of better quality.

5.2. HSV color segmentation
We perform here plant leaves segmentation using color transformations based on HSV color space
combined with morphological operations. Figure 7 shows the result of extracted maize leaves from
background.

Figure 6 : Original image of maize leaves. Figure 7 : Maize Leaves Extracted from background.
We perform here plant leaves segmentation using color transformations based on HSV color
space combined with morphological operations.
Color transformations provides a reliable means to segment maize leaves from the background, a
critical step for accurate disease detection.

5.3. Division into patches and Canny Edge Detection
The preprocessed image is divided into a specified number of equal-sized patches for localized
analysis of disease symptoms. The number of patches here is 10. Each patch undergoes Canny edge
algorithm to detect objects of interest.
Figure 8 shows the resulting image after this combined process.

19
(a) (b) (c) (d) (e)

(f) (g) (h) (i) (j)
Figure 8 : Division into 10 patches and Canny Edge Detection.

5.4. Health classification
Figure 9 below shows the health classification results generated for the 10 patches of the sample
image used above.

(a) (b) (c) (d) (e)

(f) (g) (h) (i) (j)
Figure 9: Health classification results. For each pacth generated from a to j, note a = (Patch1and
Res(Patch1)), j = (Patch10, Res(Patch10)) where Res(Patch1) is the result after Canny edge detetion
and classification as healthy (green pixels) and unhealthy (red spots (non-green diseased areas) and
double red contours for leaves damaged on edges.
Table 4 below presents the runtime of the program for a previously clear image.
Table 4
Performance Analysis of classification results of tested UAV maize leaves images.
Performance criteria Value
Runtime of the program 0.863659143447876 seconds
According to Table 4, the classification results are otained in 0,86 second less than 1 second
runtime, which ensures low computation performance of the proposed approach, crucial condition
for real-time application and decision-making.
The proposed approach addresses several key challenges in UAV-based crop monitoring. By
evaluating and correcting image blur, we ensure that the subsequent segmentation and analysis steps
are based on high-quality data. The use of adaptive Wiener filtering is particularly beneficial for real-
time applications due to its efficiency and effectiveness in varying noise conditions. Color
transformations provide a reliable means to segment maize leaves from the background, a critical
step for accurate disease detection. The division of the image into patches allows for detailed
localized analysis, making it possible to detect early signs of disease that might be missed in a full-

20
image analysis. Canny edge detection and color thresholding leverages both structural and color
information, enhancing speed and robustness of disease detection in 0,86 second.

6. Conclusion
This study presents a comprehensive real-time image processing algorithm for analyzing UAV-
captured images of maize leaves. First, we leverage adaptive Wiener filtering to address image
motion blur, then use color space transformation to segment maize leaves and finally patch-based
analysis to detect diseased regions through a combination of edge detection and color analysis. The
proposed method offers a promising solution for automated crop health monitoring, enabling timely
interventions and improving agricultural productivity, forming a fast and effective tool for precision
agriculture with less than 1 second. Future work will focus on optimizing the algorithm for different
crop types and integrating it into a real-time UAV-based monitoring system.

Acknowledgments
This work was not supported by any funding. The first author would like to acknowledge individuals
and groups that assisted in the research and the preparation of this work and is thankful to the Benin
Ministry of Higher Education and Scientific Research.
This Word template was created by Tiago Prince Sales (University of Twente, NL) in collaboration
with Manfred Jeusfeld (University of Skövde, SE). It is derived from the template designed by
Aleksandr Ometov (Tampere University of Applied Sciences, FI). The template is made available
under a Creative Commons License Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Declaration on Generative AI
The author(s) have not employed any Generative AI tools.

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