Exploring the Close-Range Detection of UAV-Based Images on Pine Wilt Disease by an Improved Deep Learning Method.

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2023-12-15 eCollection Date: 2023-01-01 DOI:10.34133/plantphenomics.0129
Xinquan Ye, Jie Pan, Gaosheng Liu, Fan Shao
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引用次数: 0

Abstract

Pine wilt disease (PWD) is a significantly destructive forest disease. To control the spread of PWD, an urgent need exists for a real-time and efficient method to detect infected trees. However, existing object detection models have often faced challenges in balancing lightweight design and accuracy, particularly in complex mixed forests. To address this, an improvement was made to the YOLOv5s (You Only Look Once version 5s) algorithm, resulting in a real-time and efficient model named PWD-YOLO. First, a lightweight backbone was constructed, composed of multiple connected RepVGG Blocks, significantly enhancing the model's inference speed. Second, a C2fCA module was designed to incorporate rich gradient information flow and concentrate on key features, thereby preserving more detailed characteristics of PWD-infected trees. In addition, the GSConv network was utilized instead of conventional convolutions to reduce network complexity. Last, the Bidirectional Feature Pyramid Network strategy was used to enhance the propagation and sharing of multiscale features. The results demonstrate that on a self-built dataset, PWD-YOLO surpasses existing object detection models with respective measurements of model size (2.7 MB), computational complexity (3.5 GFLOPs), parameter volume (1.09 MB), and speed (98.0 frames/s). The Precision, Recall, and F1-score on the test set are 92.5%, 95.3%, and 93.9%, respectively, which confirms the effectiveness of the proposed method. It provides reliable technical support for daily monitoring and clearing of infected trees by forestry management departments.

利用改进的深度学习方法探索基于无人机的松树枯萎病近距离图像检测。
松树枯萎病(PWD)是一种破坏性极大的森林病害。为了控制 PWD 的蔓延,迫切需要一种实时、高效的方法来检测受感染的树木。然而,现有的目标检测模型在兼顾轻量级设计和准确性方面往往面临挑战,尤其是在复杂的混交林中。为了解决这个问题,我们对 YOLOv5s(You Only Look Once version 5s)算法进行了改进,从而产生了一种名为 PWD-YOLO 的实时高效模型。首先,构建了一个由多个连接的 RepVGG 块组成的轻量级骨干网,大大提高了模型的推理速度。其次,设计了一个 C2fCA 模块,以纳入丰富的梯度信息流并集中于关键特征,从而保留 PWD 感染树的更多细节特征。此外,还利用 GSConv 网络代替传统的卷积,以降低网络复杂性。最后,利用双向特征金字塔网络策略加强了多尺度特征的传播和共享。结果表明,在自建的数据集上,PWD-YOLO 在模型大小(2.7 MB)、计算复杂度(3.5 GFLOPs)、参数体积(1.09 MB)和速度(98.0 帧/秒)方面都超过了现有的物体检测模型。测试集的精确度、召回率和 F1 分数分别为 92.5%、95.3% 和 93.9%,这证实了所提方法的有效性。它为林业管理部门日常监测和清除疫木提供了可靠的技术支持。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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