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A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm
Open accessAbstract
Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.
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“This advancement has far-reaching implications, allowing farmers and environmental researchers to monitor vast landscapes, particularly in remote or difficult-to-access areas, with unprecedented accuracy and speed [104].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [104]
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Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
Metadata
- Authors
- Jayagopal, P., Purushothaman Janaki, K., Mohan, P., Kondapaneni, U. B., Periyasamy, J., Mathivanan, S. K., & Dalu, G. T.
- Affiliations
- Vellore Institute of Technology University (IN)
- Bharath University (IN)
- Galgotias University (IN)
- Haramaya University (ET)
- Corresponding author
- Dalu, Gemmachis Teshite
- Venue
- Scientific Reports
- Published
- 2024-02-27
- Volume
- 14
- Issue
- 1
- Pages
- 4814-4814
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10899584
- OpenAlex
- W4392189094
- Cited by
- 13
- References
- 2
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 4
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- aerial imagery, YOLOv5, tabu search, tree disease detection, synthetic data generation, data scarcity, wildfire monitoring, object detection, tree identification, real-time detection
- MeSH terms
- Algorithms, Trees, Ecosystem, Climate Change, Forests
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