School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India.
Sci Rep. 2024 Feb 27;14(1):4814. doi: 10.1038/s41598-024-54399-w.
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.
我们的环境受到气候变化的严重影响。根据之前的研究，全球气候变化引起的昆虫灾害使许多树木死亡，不可避免地导致了森林火灾。森林状况是森林火灾的一个重要指标。对森林的航空图像进行分析可以早期发现死亡和存活的树木。自动化的森林健康诊断对于监测和保护森林生态系统健康至关重要。本文提出了一种使用航空图像评估森林健康的新方法，即结合改进生成对抗网络（MGAN）和 YOLOv5（单次检测版本 5）。我们还采用禁忌搜索算法（TSA）来增强识别和分类不健康森林区域的过程。所提出的模型提供了合成数据来补充有限的标记数据集，从而解决了森林健康诊断任务中经常出现的数据稀缺问题。这一改进提高了模型对以前未观察到的数据进行泛化的能力，从而提高了森林健康评估的整体精度和鲁棒性。此外，YOLOv5 的集成使实时对象识别成为可能，使模型能够以极高的速度和准确性识别和定位众多树种和潜在的健康问题。YOLOv5 的高效架构使其能够在资源有限的设备上部署，从而实现现场森林监测应用。我们使用 TSA 来增强对不健康森林区域的识别。TSA 方法有效地调查了搜索空间，确保模型收敛到接近最优的解决方案，提高了疾病检测的精度，减少了误报。我们使用大量不同森林栖息地的航空图像数据集来评估我们的 MGAN-YOLOv5 方法。实验结果表明，该方法在自动诊断森林健康方面表现出色，检测精度达到 98.66%，召回率达到 99.99%，F1 分数达到 97.77%，准确率达到 99.99%，响应时间为 3.543ms，计算时间为 5.987ms。值得注意的是，与所有比较的目标检测方法相比，我们的方法的 mAP 至少提高了 2%。
