Deep Learning-Based Pine Nematode Trees’ Identification Using Multispectral and Visible UAV Imagery

Qin, Bingxi; Sun, Fenggang; Shen, Weixing; Dong, Bin; Ma, Shencheng; Huo, Xinyu; Lan, Peng

doi:10.3390/drones7030183

Cited by 18 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These attention mechanisms enable the network to capture finer target-related details, thereby improving the model’s ability to perceive information. Qin et al ( Qin et al., 2023 ). achieved high accuracy in detecting pine wilt diseased trees based on improved YOLOv5 combined with attention mechanisms such as CBAM.…”

Section: Discussionmentioning

confidence: 99%

Pine wilt disease detection algorithm based on improved YOLOv5

Du,

Wu,

Wen

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Pine wilt disease (PWD) poses a significant threat to forests due to its high infectivity and lethality. The absence of an effective treatment underscores the importance of timely detection and isolation of infected trees for effective prevention and control. While deep learning techniques combined unmanned aerial vehicle (UAV) remote sensing images offer promise for accurate identification of diseased pine trees in their natural environments, they often demand extensive prior professional knowledge and struggle with efficiency. This paper proposes a detection model YOLOv5L-s-SimAM-ASFF, which achieves remarkable precision, maintains a lightweight structure, and facilitates real-time detection of diseased pine trees in UAV RGB images under natural conditions. This is achieved through the integration of the ShuffleNetV2 network, a simple parameter-free attention module known as SimAM, and adaptively spatial feature fusion (ASFF). The model boasts a mean average precision (mAP) of 95.64% and a recall rate of 91.28% in detecting pine wilt diseased trees, while operating at an impressive 95.70 frames per second (FPS). Furthermore, it significantly reduces model size and parameter count compared to the original YOLOv5-Lite. These findings indicate that the proposed model YOLOv5L-s-SimAM-ASFF is most suitable for real-time, high-accuracy, and lightweight detection of PWD-infected trees. This capability is crucial for precise localization and quantification of infected trees, thereby providing valuable guidance for effective management and eradication efforts.

show abstract

Section: Discussionmentioning

confidence: 99%

Pine wilt disease detection algorithm based on improved YOLOv5

Du,

Wu,

Wen

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…The above research projects are based on image processing and recognition of RGB images, while some groups leverage the high dimensional feature in multi-spectral images. Qin et al [83] and Park [84] use drones mounted with multi-spectral cameras for training data collection and conducting deep neural network detection.…”

Section: Pine Wilt Detectionmentioning

confidence: 99%

Deep learning approaches for object recognition in plant diseases: a review

Zhou,

Zhang,

et al. 2023

Intelligence & Robotics

View full text Add to dashboard Cite

Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. In recent years, the intersection of agriculture and artificial intelligence has become a popular research topic. Researchers have been experimenting with object recognition algorithms, specifically convolutional neural networks, to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

show abstract

“…UAVs have been used in remote sensing to detect and segment numerous types of objects and different environments, such as agricultural fields, urban areas, forests, and bodies of water, providing valuable data for various applications including environmental monitoring, disaster management, and infrastructure inspection [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. These technological tools, often accompanied by classical machine learning (ML) and deep learning (DL) methodologies, enhance the accuracy and efficiency of different vegetation mapping [ 18 , 19 , 20 , 21 , 22 , 23 ]. Studies have demonstrated the effectiveness of employing DL for accurately monitoring and classifying these delicate species like moss and lichen in diverse environmental settings [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Raniga,

Amarasingam,

Sandino

et al. 2024

Sensors

View full text Add to dashboard Cite

Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.

show abstract

Deep Learning-Based Pine Nematode Trees’ Identification Using Multispectral and Visible UAV Imagery

Cited by 18 publications

References 21 publications

Pine wilt disease detection algorithm based on improved YOLOv5

Pine wilt disease detection algorithm based on improved YOLOv5

Deep learning approaches for object recognition in plant diseases: a review

Monitoring of Antarctica’s Fragile Vegetation Using Drone-Based Remote Sensing, Multispectral Imagery and AI

Contact Info

Product

Resources

About