1. Introduction
Populus nigra, or black
poplar, is a critical species in global riparian forest ecosystems. Historically, black
poplar was abundant across Europe, but its numbers have dramatically decreased due to human activities such as river management, diseases, and wood cutting. This reduction is alarming because black
poplar plays a vital role not only ecologically—contributing to the biodiversity and stability of river habitats—but also economically, as it is used in soil protection and reforestation efforts in polluted areas [
1]. The planting material of poplar is of great significance in raising plantations. Only source-identified planting material should be used. This is because
poplars are raised clonally, and there are several thousand clones in cultivation. They are site-specific and locality-specific. Their wood density is also variable. They are specific to a particular day, length of light, and temperature. It is not easy to identify a clone at the nursery stage unless one inspects them daily and records their characteristics [
2]. From an economic perspective,
poplars are used in the timber industry for products like paper and plywood due to their rapid growth and regeneration capabilities. They are also used in bioenergy production and phytoremediation projects due to their ability to accumulate heavy metals and other contaminants, helping to clean up polluted soils and water. Moreover, studies on
poplars have helped in explaining the storage and mobilization of nutrients within trees, offering insights into tree physiology that are crucial for forestry management and conservation efforts. The ability of
poplars to adapt to different environments and their role in carbon sequestration further underline their environmental importance [
3].
Poplars are susceptible to various diseases and pests that can decimate populations, especially when trees are stressed by other environmental factors. Reduced genetic diversity is caused by human activities like selective logging and habitat fragmentation, which can lead to reduced genetic diversity in
poplar populations, making them more vulnerable to diseases, pests, and environmental changes [
4]. Detecting these diseases early is crucial for their preservation. There are many methods to detect the diseases in trees, for example, visual inspection, laboratory testing, remote sensing, and aerial imaging. Identifying symptoms through expert analysis is both time-intensive and frequently too delayed to allow for effective treatment [
5]. Regular monitoring and surveillance of
poplar stands are crucial for the early detection of disease, pest infestation, or environmental stress. Early detection allows for timely interventions and can help prevent the spread of pathogens. Maintaining and enhancing genetic diversity within
poplar populations also play a vital role in increasing their resilience to pests and diseases. This can be achieved by planting a mix of genetically diverse individuals that are resistant to various diseases and environmental stresses. Sanitation and hygiene practices such as removing diseased and dead trees and cleaning pruning tools can minimize the spread of pathogens. Infected plant materials should be disposed of safely and away from healthy trees. In cases where it is necessary, the use of fungicides and pesticides can control the spread of certain diseases and pests, although these should be used judiciously to minimize environmental impacts and avoid the development of resistance. Biological controls, such as introducing natural predators or beneficial microorganisms that combat pests and diseases, offer an environmentally friendly alternative to chemical treatments. Implementing quarantine measures and restricting the movement of plant materials from areas known to be infested can prevent the introduction and spread of pathogens and pests to new areas. Furthermore, developing disease-resistant poplar varieties through traditional breeding or genetic engineering provides a long-term sustainable solution. This process involves selecting traits that enhance resistance to specific diseases and environmental stresses. Educating and training forestry staff and stakeholders on the identification of diseases, appropriate treatment methods, and management practices can also greatly enhance the effectiveness of disease control strategies [
6].
Deep learning (DL) and Convolutional Neural Networks (CNNs) have been extensively utilized in recent research [
7]. They offer a revolutionary approach to identifying and diagnosing these diseases early on, thereby allowing for timely intervention and treatment [
8]. By training models on images of
poplar leaves, branches, and bark exhibiting signs of infection, these algorithms can learn to recognize the subtle nuances that differentiate healthy from diseased trees. One of the key advantages of using CNN architectures in disease detection is their ability to process and analyze complex image data, identifying patterns and features that may not be visible to the human eye [
9]. This capability is particularly beneficial for detecting diseases that manifest through minor changes in leaf color, texture, or pattern. There has been significant research conducted to detect plant diseases, with a focus on
poplar diseases. A review of the existing literature underscores a significant hurdle in progressing research within this domain: the scarcity of natural or real-world datasets for
poplar diseases. Furthermore, existing real-world datasets that could potentially be useful are privately held and not available to the broader research community.
Our preliminary research efforts focused on identifying diseases affecting Poplar (Populus) trees revealed that the predominant issues include leaf miner infections, spotted wilt virus, and nutritional deficiencies. As a result of the studies, we found that the most important poplar leaf diseases are those mentioned above, “Parsha (Scab)”, “Brown-spotting”, “White-Gray spotting”, and “Rust”. Regrettably, there are no publicly accessible datasets that encompass these specific disease categories, and we also noted instances where a single leaf exhibited multiple disease afflictions. To mitigate these challenges and advance research in this field, we propose the development of a novel dataset named “Poplar-Disease”. This initiative aims to be the first to offer a publicly available dataset that includes the multilabelling technique, object detection data specifically targeting real-world cases of poplar trees affected by leaf miner, spotted wilt virus, and nutritional deficiencies. In conjunction with this new dataset, we also implemented a comprehensive poplar disease detection system designed to operate effectively in real-world environments. This system and the accompanying dataset are poised to enhance diagnostic accuracy and facilitate more effective management of Poplar (Populus) diseases.
Our study introduces, as described in
Figure 1, significant advancements in
poplar disease detection through the development of a YOLOv8 model, supported by a newly compiled dataset. The key contributions include the following:
Data Collection: Gathering a diverse and comprehensive collection of images featuring poplar trees affected by various diseases. This involved extensive fieldwork and the compilation of images from various sources to create a robust dataset for analysis.
Expert Classification: The images were expertly classified and labeled, ensuring the dataset’s accuracy and reliability for model training.
Generating New Poplar Disease-Based Dataset: Using the collected images to create a new dataset specifically focused on poplar diseases. This dataset includes labeled images where each image is annotated with information about the specific diseases affecting the poplar trees depicted.
Powerful Model Training (YOLO): Utilizing state-of-the-art deep learning techniques, such as the YOLO (You Only Look Once) model, for training a powerful machine learning model. YOLO is known for its speed and accuracy in object detection tasks, making it well suited for identifying and localizing diseases in images of poplar trees leaves.
These contributions aim to facilitate early disease detection and support ongoing research, displaying our commitment to improving plant health monitoring. The structure of this paper on
poplar disease detection is methodically organized to facilitate understanding and exploration of the topic, as follows:
Section 2 delves into a review of existing studies, focusing on the methods traditionally employed for identifying specific properties and indicators of
poplar diseases. In
Section 3, we introduce our proposed approach to
poplar disease detection, detailing the methodology, the development of the YOLOv8 model, and the compilation of a comprehensive dataset.
Section 4 presents the results of our experiments conducted using the newly assembled database, providing a thorough analysis and discussion of the findings.
Section 5 addresses the limitations and future work encountered with the proposed method, offering insights into the challenges and potential areas for refinement. Concluding the paper,
Section 6 summarizes our main contributions and outcomes, while also outlining directions for future research to further advance the field of
poplar disease detection.
3. Materials and Methods
3.1. Data Collection and Preprocessing
Undoubtedly, the most crucial aspects of training a model in AI are a high-quality dataset and a robust model. Even if we have modern models, the lack of sufficient data can pose a significant problem. This was precisely our case since we could not find a quality dataset or even images of
poplar leaves from officially open sources. Consequently, we embarked on creating our quality dataset, understanding its potential to aid future researchers. Considering the different environments, we collected poplar leaves from three regions of Uzbekistan and South Korea, namely Samarkand, Navoiy, and Jizzakh, during the months of August and September. These months were chosen because the
poplar leaves are sufficiently grown and can provide images clear enough for the model to detect diseases. Following the advice of our experts, we started collecting images of both diseased and healthy
poplar leaves. For this task, we utilized the latest and most powerful digitals with cameras with 12 MP and an f/1.6 aperture of Samsung, South Korea, which can capture high-quality images. This technology ensures excellent photo clarity and detail, even in low-light conditions. We managed to collect 2357 diseased and healthy poplar leaves. After a 20-day collection period, our experts manually classified the leaves into healthy and diseased categories, resulting in 362 healthy leaves and 1995 diseased ones. With expert assistance, we began labeling the diseased leaves, ensuring each was accurately labeled. Working together, we focused on identifying four diseases, which are described in
Figure 2: “Parsha (Scab)”, “Brown Spotting”, “White-Gray Spotting”, and “Rust”. See
Table 1.
We used the Make Sense AI [
26], choosing the polygon structure to label the diseased spots accurately. It was crucial to note that some leaves showed signs of two or more diseases, each of which was labeled separately. The annotated values were saved in JSON files that store the coordinates of the disease in the image. We downloaded our labeled data in JSON format from Make Sense AI and used Roboflow [
27] for augmentation, specifically 90-degrees clockwise and counterclockwise rotations, and rotations from −15 to +15 degrees [
28], employing Computer Vision techniques for this purpose. This process ensured the data, now increased to 6155 images as shown in
Table 2, were in the necessary format, specifically txt, because we train YOLO with txt-formatted data.
This step was vital to avoid any color alterations, as maintaining the diseased part of the leaf in its original state is crucial for accurate disease detection. After applying these technologies, the image count reached 6155. As shown in
Figure 3 and
Figure 4, all images were labeled by us, augmented, and increased in number to enhance the machine’s learning capability.
3.2. Proposed Method and Model Architecture
Our primary objective is to enhance the detection of diseases in
poplar leaves by leveraging a sophisticated detection model that has been fine-tuned using YOLOv8 technology. The challenge of identifying diseases in plants lies in the minuscule size and often indiscernible characteristics of the affected areas on the leaves. These areas frequently blend into their surroundings due to the varied shapes and sizes they can take on, which in turn can diminish the clarity and contrast of the images captured. To solve these issues and improve the quality of the images, we employ Contrast Stretching [
29], a technique that broadens the spectrum of intensity levels in images of diseased leaves, thereby making the diseased spots more distinguishable. The reliance of object detection algorithms on distinct features and patterns means that variability in the appearance of leaves and symptoms can significantly hinder the performance of traditional detection models. Moreover, the necessity for real-time processing to analyze a continuous influx of data, such as video streams, highlights the need for models that can rapidly process information without substantial delays. Sluggish processing speeds could result in a disconnect between the detection of a disease and the response to it. Given these challenges, the adoption of YOLOv8 is a strategic choice due to its efficiency and effectiveness in handling diverse aspect ratios and ensuring prompt disease detection. This approach aims to mitigate the limitations associated with conventional object detection methods and to advance the promptness and accuracy of
poplar disease identification.
3.3. The Model Structure of YOLOv8 Network
YOLOv8 represents the latest advancement in the YOLO (You Only Look Once) series, setting new benchmarks in real-time object detection and classification. It is designed to process images in a single evaluation, drastically reducing the time needed for detection while maintaining high accuracy. YOLOv8 enhances its predecessors by optimizing both the speed and precision of detection, making it capable of identifying and classifying objects in complex environments with remarkable efficiency. This model integrates advanced neural network architectures and machine learning techniques to improve upon the limitations of earlier versions, offering a more robust solution for various applications in surveillance, autonomous vehicles, and beyond.
Figure 5 illustrates the YOLOv8 architecture. The architecture uses a modified CSPDarknet53 backbone. The C2f module replaces the CSPLayer used in YOLOv5. A spatial pyramid pooling fast (SPPF) layer accelerates computation by pooling features into a fixed-size map. Each convolution has batch normalization and SiLU activation. The head is decoupled to process objectness, classification, and regression tasks independently.
3.4. Techniques for Enhancing Image Quality
Enhancing the quality of images is crucial for various applications in photography, Computer Vision, and digital imaging. Several techniques can be used to improve image contrast, sharpness, and overall visual appeal. Here are some notable techniques:
Contrast Stretching enhances the contrast of an image by expanding the range of intensity values. This method transforms the pixel values so that the minimum and maximum intensity values of the original image are stretched to cover the full range of possible intensity values, typically from 0 to 255 for an 8-bit grayscale image. This technique helps in improving the visibility of features in the image. Histogram Equalization improves the contrast of an image by redistributing the intensity values so that the histogram of the output image is approximately flat. This method enhances the global contrast of the image and is particularly useful for images with backgrounds and foregrounds that are both bright or both dark. Adaptive Histogram Equalization (CLAHE) enhances local contrast and improves the definition of edges in each region of an image. Unlike standard histogram equalization, which works on the entire image, CLAHE operates on small regions (tiles) in the image, making it more effective for images with varying lighting conditions. Gamma Correction adjusts the brightness of an image by applying a power-law transformation. It is used to enhance both dark and bright regions of an image. By choosing an appropriate gamma value, the visibility of details in different intensity ranges can be significantly improved. Unsharp Masking enhances the edges of an image, making it appear sharper. It works by subtracting a blurred version of the image from the original image, which enhances the edges while preserving the overall smoothness of the image [
31,
32,
33,
34,
35].
3.5. Contrast Stretching Technique
Applying the Contrast Stretching technique to enhance the visibility of poplar leaf images is one of the key strategies in our poplar disease detection study. Given that poplar trees are often situated in environments prone to humidity, our goal is to sharpen the accuracy in detecting poplar diseases according to the features of the leaves.
High humidity can lead to diminished image quality, affecting the detection system’s ability to perform accurately due to issues like low visibility, image blurring, and changes in leaf surface appearance. To counteract these challenges, we incorporated the Contrast Stretching technique [
36] into our
poplar disease detection model. Before the technique can be performed, it is necessary to specify the upper- and lower-pixel value limits over which the image is to be normalized. Often, these limits will just be the minimum and maximum pixel values that the image type concerned allows. For example, for 8-bit gray-level images, the lower and upper limits might be 0 and 255. Call the lower and the upper limits
a and
b, respectively. As shown in
Figure 6, the simplest sort of normalization then scans the image to find the lowest and highest pixel values currently present in the image. Then, each pixel
P is scaled using the following function:
Values below 0 are set to 0, and values around 255 are set to 255.
The problem with this, as illustrated on (1) formula, is that a single outlying pixel with either a remarkably high or incredibly low value can severely affect the value of c or d, and this could lead to very unrepresentative scaling. Therefore, a more robust approach is to first take a histogram of the image and then select c and d at, say, the 5th and 95th percentile in the histogram (that is, 5% of the pixel in the histogram will have values lower than c, and 5% of the pixels will have values higher than d). This prevents outliers from affecting the scaling too much.
Another common technique for dealing with outliers is to use an intensity histogram to find the most popular intensity level in an image (i.e., the histogram peak) and then define a cutoff fraction, which is the minimum fraction of this peak magnitude below which data will be ignored. The intensity histogram is then scanned upward from 0 until the first intensity value with contents above the cutoff fraction. This defines c. Similarly, the intensity histogram is then scanned downward from 255 until the first intensity value with contents above the cutoff fraction. This defines d.
4. Experimental Results
This study lays solid groundwork for the successful identification of diseases on poplar leaves, while also highlighting the substantial promise of Computer Vision and DL techniques in tackling pivotal health issues in agriculture. Through detailed assessment using metrics such as Precision, Recall, and F1 score, we gain in-depth insight into the model’s efficacy, confirming its suitability for real-world application in poplar disease management. Moreover, the encouraging results from our initial trials prompt ongoing research and enhancement of these techniques, aiming to significantly improve poplar disease detection for widespread agricultural use.
4.1. Model Evaluation
The evaluation of our model is rigorously conducted through the analysis of a confusion matrix, an essential instrument for interpreting detection accuracy. This matrix contrasts the model’s predictions with the actual labels, revealing its precision in differentiating between diseased and healthy leaf samples. The choice of performance indicators depends on specific aspects such as data attributes and research goals. These indicators are crucial in measuring the effectiveness of our approach, providing a detailed assessment of the model’s capabilities. We closely examine essential metrics, like True Positives (TPs), True Negatives (TNs), False Positives (FPs), and False Negatives (FNs) [
32,
33,
34,
35,
36,
37,
38,
39,
40]. These metrics capture the model’s ability to correctly identify the conditions of the leaves.
Calculating these metrics allows for an in-depth evaluation of the model’s performance, illustrating its potential utility and dependability in various situations.
4.2. Model Training Results
In the realm of object detection, the YOLO (You Only Look Once) architecture has been extensively employed for recognizing both moving and stationary objects, including in applications, like identifying vehicle license plates, pedestrians, wildlife, and detecting affected spots of leaves on trees for their health. This versatility is due to YOLO’s utilization of Deep Convolutional Neural Networks (DCNNs) to learn and detect object features in poplar (Populus) tree diseases, a challenge we aimed to address in our study with a newly generated dataset, which contains 6155 diseased images. We meticulously set up, as illustrated in
Table 3, a formidable workstation equipped with an AMD Ryzen 5 7500F processor and bolstered by 32 GB of RAM, alongside an NVIDIA graphics card, made in TSMC (Taiwan Semiconductor Manufacturing Company), Taiwan, ensuring that the hardware is primed for intensive computation. With the system anchored by Ubuntu 22.04.4 LTS, we installed and updated the CUDA toolkit and cuDNN libraries to harness the full potential of GPU acceleration.
Leveraging deep learning frameworks such as PyTorch, TensorFlow, and others, after successful training, the model underwent thorough evaluation and yielded the following results:
The charts, displayed in
Figure 7,
Figure 8,
Figure 9,
Figure 10 and
Figure 11, illustrate the performance metrics, including mean Average Precision (mAP), Precision, and Recall, for a training dataset comprising 6155 samples. These graphs are generated based on the calculated Precision–Confidence Curve and Precision–Recall Curve, with Recall–Confidence Curve values. We dedicated significant resources to train our YOLOv8-based model, completing 10,000 epochs over 261 h, which showed good results, with the model demonstrating a prominent level of proficiency in detecting diseased spots on
poplar leaves.
Figure 7 shows a Precision–Confidence Curve. It demonstrates that the model’s predictions are more reliable at higher confidence levels across all classes. A0_PARSHA and A1_RUST exhibit strong performance, even at moderate confidence levels, while A1_BROWN SPOTTING and A2_WHITE/GRAY SPOTTING show significant improvements in precision at higher confidence levels. The overall high precision at a confidence level of 0.857 suggests that the model is well calibrated and can be trusted for making high-confidence predictions.
Figure 8 illustrates a Precision–Recall Curve. It describes the model’s ability to balance precision and recall for each class. A0_PARSHA and A1_RUST exhibit outstanding performance with high precision and recall values, as evidenced by their high AP scores. A1_BROWN SPOTTING and A2_WHITE/GRAY SPOTTING show more significant trade-offs, with lower precision at higher recall levels, indicating areas where the model could potentially improve. The high mAP of 0.866 for all classes suggests that the model performs well overall, maintaining a good balance between precision and recall across different classification tasks.
The Recall–Confidence Curve highlights, in
Figure 9, the model’s ability to maintain recall across different confidence thresholds for each class. A1_RUST shows exceptional performance with nearly perfect recall across all confidence levels. A0_PARSHA also performs well, maintaining high recall up to moderate confidence levels. However, A1_BROWN SPOTTING and A2_WHITE/GRAY SPOTTING show significant declines in recall as confidence increases, indicating that the model tends to miss more True Positives for these classes at higher confidence thresholds. Overall, the model achieves a high recall of 0.92 at a confidence level of 0.0 for all classes, demonstrating its effectiveness in capturing True Positives at low confidence levels.
Figure 10 shows the F1–Confidence Curve and the model’s effectiveness in balancing precision and recall across different confidence levels for each class. A0_PARSHA and A1_RUST exhibit outstanding performance, maintaining high F1 scores across a broad range of confidence levels. A1_BROWN SPOTTING and A2_WHITE/GRAY SPOTTING show a more typical pattern, with optimal performance at moderate confidence levels but reduced F1 scores at higher confidence levels. The overall model performance, represented by the dark-blue curve, peaks at an F1 score of 0.85 at a confidence level of 0.239, suggesting this is the optimal confidence threshold for a balanced performance across all classes.
Figure 11 shows a pair plot, which is a matrix of scatter plots used to visualize the relationships between multiple variables. Here is a description of the plot:
The plot includes four variables: x, y, width, and height. The diagonal plots show the distribution of each variable as a histogram or density plot. The off-diagonal plots are scatter plots showing the relationships between pairs of variables. For instance, the scatter plot between x and y is shown in the top-left corner. A scatter plot between width and height is in the bottom-right corner. Color and Density: The scatter plots use a blue color to represent data points, with darker areas indicating a higher density of points. Axes: Each scatter plot has its own set of x and y axes, labeled according to the variables being plotted.
Impressively, as illustrated in
Figure 12, it achieved an average accuracy rate of 95% in successful detections. Despite its powerful performance, the model remains highly efficient with a compact size of just 6.9 MB of best.pt, making it ideal for deployment in field applications, where storage and processing power may be limited.
Table 4 illustrates a performance comparison of some YOLO generations, which we used to compare our model with.
As illustrated in
Table 4, the proposed method outperforms both YOLO7 and YOLOv8 across all evaluated metrics (mAP, Precision, Recall, and Testing Accuracy), indicating superior performance in terms of accuracy and reliability in detecting objects.
6. Conclusions
In this study, we developed and tested a novel approach for identifying diseases in poplar leaves using the advanced capabilities of the YOLOv8 model. By manually assembling a unique dataset and applying the Contrast Stretching method, we enhanced the model’s ability to detect subtle variations indicative of disease. The results from our experiments confirm that YOLOv8, though primarily designed for other forms of object detection, is exceptionally effective in the agricultural domain, particularly for plant disease detection. Our implementation of Contrast Stretching improved the model’s robustness, allowing for more accurate disease detection by enhancing feature distinction within the images. The tailored dataset was crucial in training the model to recognize and classify various disease patterns specifically found in poplar leaves. The performance comparison demonstrated the significant improvement achieved with our proposed method. Specifically, our method achieved a mean Average Precision (mAP) of 86.6%, Precision of 85.7%, Recall of 92%, and Testing Accuracy of 95%. In comparison, YOLOv8 achieved a mAP of 73.2%, Precision of 75.5%, Recall of 72.8%, and Testing Accuracy of 87%. This highlights the substantial advancements made by our approach. As we look to the future, our focus will be on refining the accuracy and reliability of disease detection. We plan to incorporate the next generation of YOLO technology to address existing challenges such as blurring and the accurate differentiation of similar colors. This will involve not only hardware improvements but also further software optimizations to enhance our model’s diagnostic capabilities. These advancements will pave the way for real-time, field-level disease monitoring, which is essential for timely and effective disease management in poplar cultivation.