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.
Plant disease detection is a key application of UAVs and has been extensively researched [
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 [
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.
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.
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.
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.
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.
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 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 where I is the grayscale image, μ is the mean of the Laplacian image, and N is the total number of The variance provides a measure of image sharpness, with lower values indicating higher blur 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 steps. pixels. levels. (1) (2) (3) grayscale image:
( , ) − ) follows:
Normalize grayscale image: = Apply Wiener filter
Denormalize: 3.1.2. Image Segmentation = ( − ) ⋅ (
) + = 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: ℎ where 1 and
Morphological operations, including opening and closing, are applied to the binary mask to remove noise and smooth the segmented regions.
( ) = ( ⊙ ) ⊕ where ⊙ denotes morphological opening and ⊕ denotes closing, and is the structuring 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) ; = where is the height and is the width of the patch where G(x,y) is the gradient magnitude at pixel (x,y).
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. 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: (13) (14) (15) where
Following equations explain the classification process: where is the pixel value within a contour. is fixed here to 500.
> 3: σ² ← clv(I(x, y)) -- Calculate the Laplacian variance to assess image sharpness
I(x, y) ← avf(I(x, y)) -- Apply the adaptive Wiener filter if Variance < threshold 4: if σ² < Threshold then 5: 6: end if 12: 13: 14: end For 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
Edge [ ] ← ced(patch) -- Canny edge detection of each patch
Cntrs [ ] ← fdc(Edge [ ]) -- Classify detected edges based on health status
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.
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. plants leaves used for constructing the dataset [18].
The images were accessed on 14 July 2024 and acquired on board the UAV, in the village of DodjiSèhè inside Sekou in the town of Allada, Benin Republic. Following Figure 2 and Figure 3 give additional details on the site of study.
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.
Below we present a sample of image from the dataset with preprocessing steps as follows: (a)
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.
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. (f) (g) (h) Figure 8 : Division into 10 patches and Canny Edge Detection.
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.
This study presents a comprehensive real-time image processing algorithm for analyzing UAVcaptured 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.
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.
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