A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

Li, Fengdi; Liu, Zhenyu; Shen, Weixing; Wang, Yan; Wang, Yunlu; Ge, Chengkai; Sun, Fenggang; Lan, Peng

doi:10.1109/access.2021.3073929

Cited by 40 publications

(32 citation statements)

References 29 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The researchers barely addressed the issue of excessive data storage within the literature dataset. Only in one paper was redundant drone imagery filtered in real time to reduce processing efforts [124]. The same applies to the flight altitude, which is rarely experimented with.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In some cases, spectral data were enriched with structural information derived from point clouds, DSMs or CHMs based on either LiDAR [116][117][118] or image data [96,102,119,120], further improving classification results. In a few papers, the raw drone images were directly analyzed without further processing [44,108,[121][122][123][124][125][126]. To create photogrammetric products, the researchers predominantly implemented commercial SfM software such as Agisoft Metashape (Agisoft LLC, St. Petersburg, Russia) and Pix4D (Pix4D S.A., Lausanne, Switzerland).…”

mentioning

confidence: 99%

See 1 more Smart Citation

UAV-Based Forest Health Monitoring: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

show abstract

Section: Data Acquisitionmentioning

confidence: 99%

mentioning

confidence: 99%

UAV-Based Forest Health Monitoring: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Coupled with edge computing technology (a means of on-board data processing for remote devices), these technologies provide an elegant solution to process large image-based data files into compressed, processed data for transmission at lower costs. For example, the coupling of remote sensing and edge computing to process images and videos has been implemented within unmanned aerial vehicles to manage disease outbreaks (Li et al, 2021) and in remote underwater videos to detect and record large mobile animals (Coro and Walsh, 2021). There are still challenges in data transfer and storage that limit the scalability of these technologies such as the relatively short range of transfer, high noise, and limited bandwidth capacity of underwater wireless sensor networks which are unique challenges in marine environments (Coutinho et al, 2018).…”

Section: Data Transfer and Storagementioning

confidence: 99%

Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

et al. 2022

View full text Add to dashboard Cite

Conservation of marine ecosystems has been highlighted as a priority to ensure a sustainable future. Effective management requires data collection over large spatio-temporal scales, readily accessible and integrated information from monitoring, and tools to support decision-making. However, there are many roadblocks to achieving adequate and timely information on both the effectiveness, and long-term success of conservation efforts, including limited funding, inadequate sampling, and data processing bottlenecks. These factors can result in ineffective, or even detrimental, management decisions in already impacted ecosystems. An automated approach facilitated by artificial intelligence (AI) provides conservation managers with a toolkit that can help alleviate a number of these issues by reducing the monitoring bottlenecks and long-term costs of monitoring. Automating the collection, transfer, and processing of data provides managers access to greater information, thereby facilitating timely and effective management. Incorporating automation and big data availability into a decision support system with a user-friendly interface also enables effective adaptive management. We summarise the current state of artificial intelligence and automation techniques used in marine science and use examples in other disciplines to identify existing and potentially transferable methods that can enable automated monitoring and improve predictive modelling capabilities to support decision making. We also discuss emerging technologies that are likely to be useful as research in computer science and associated technologies continues to develop and become more accessible. Our perspective highlights the potential of AI and big data analytics for supporting decision-making, but also points to important knowledge gaps in multiple areas of the automation processes. These current challenges should be prioritised in conservation research to move toward implementing AI and automation in conservation management for a more informed understanding of impacted ecosystems to result in successful outcomes for conservation managers. We conclude that the current research and emphasis on automated and AI assisted tools in several scientific disciplines may mean the future of monitoring and management in marine science is facilitated and improved by the implementation of automation.

show abstract

“…In addition, Reference [20] presented a dataset with multi-band images and built a spatial-context-attention network (SCANet) with an expanded receptive field to better utilize context information. Further, Reference [21] proposed a faster disease filtering method that employs a lightweight one-stage detection model that discards a large number of irrelevant images before classifying the rest.…”

Section: Deep Learning-based Pwd Detectionmentioning

confidence: 99%

“…We collected a large dataset consisting of observations from various districts in South Korea. The suppression of ambiguous background objects is particularly important in this domain [21]. The hard negative samples for PWD were further divided into six categories according to their appearance and texture information.…”

Section: System Overviewmentioning

confidence: 99%

A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images

Jie

Lee

2021

Remote Sensing

View full text Add to dashboard Cite

Pine wilt is a devastating disease that typically kills affected pine trees within a few months. In this paper, we confront the problem of detecting pine wilt disease. In the image samples that have been used for pine wilt disease detection, there is high ambiguity due to poor image resolution and the presence of “disease-like” objects. We therefore created a new dataset using large-sized orthophotographs collected from 32 cities, 167 regions, and 6121 pine wilt disease hotspots in South Korea. In our system, pine wilt disease was detected in two stages: n the first stage, the disease and hard negative samples were collected using a convolutional neural network. Because the diseased areas varied in size and color, and as the disease manifests differently from the early stage to the late stage, hard negative samples were further categorized into six different classes to simplify the complexity of the dataset. Then, in the second stage, we used an object detection model to localize the disease and “disease-like” hard negative samples. We used several image augmentation methods to boost system performance and avoid overfitting. The test process was divided into two phases: a patch-based test and a real-world test. During the patch-based test, we used the test-time augmentation method to obtain the average prediction of our system across multiple augmented samples of data, and the prediction results showed a mean average precision of 89.44% in five-fold cross validation, thus representing an increase of around 5% over the alternative system. In the real-world test, we collected 10 orthophotographs in various resolutions and areas, and our system successfully detected 711 out of 730 potential disease spots.

show abstract

A Remote Sensing and Airborne Edge-Computing Based Detection System for Pine Wilt Disease

Cited by 40 publications

References 29 publications

UAV-Based Forest Health Monitoring: A Systematic Review

UAV-Based Forest Health Monitoring: A Systematic Review

Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images

Contact Info

Product

Resources

About