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 decisionmaking, 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].

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.

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. Compares the distribution of colors in an image using a threshold.

-Simple to implement -Fast computation -Limited discriminative power -Sensitive to changes in lighting conditions [1] Texture Analysis Analyzing textural patterns in an image to characterize healthy and unhealthy areas.

-Captures subtle differences in texture -Robust against changes in lighting and color -Parameter tuning required -May be computationally intensive [2] Machine Learning Models Utilizing machine learning algorithms (e.g., SVM, Random Forest, CNN) to learn features and classify healthy and unhealthy areas.

-High accuracy and robustness -Can automatically learn complex patterns -Requires large amounts of labeled data -Training and inference can be computationally costly [3] 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.

Proposed Approach

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.

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.

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)

 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)

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:

𝑀𝑎𝑠𝑘 = 1 𝑖𝑓 𝑟𝑎𝑛𝑔𝑒1 ≤ 𝐻𝑆𝑉(𝑥, 𝑦) ≤ 𝑟𝑎𝑛𝑔𝑒2 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(9)

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 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:

𝐸𝑑𝑔𝑒𝑠(𝑥, 𝑦) = 1 𝑖𝑓 𝐺(𝑥, 𝑦) > 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(13)

where G(x,y) is the gradient magnitude at pixel (x,y).

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.

𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 = 𝐻𝑒𝑎𝑙𝑡ℎ𝑦 𝑖𝑓 𝐴𝑟𝑒𝑎 > 𝐴 𝑈𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (15)

where 𝐴 is fixed here to 500.

Below is the detailed algorithm:

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.

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].

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.

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).

Results and discussion

Preprocessing steps

Below we present a sample of image from the dataset with preprocessing steps as follows: 3 indicates the performances characteristics of the deblurred image.

(a) (b) (c)

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. 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.

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. 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.

Health classification

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 realtime 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-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.

Conclusion

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.