GSEYOLOX-s: An Improved Lightweight Network for Identifying the Severity of Wheat Fusarium Head Blight

Mao, Rui; Wang, Zhengchao; Li, Feilong; Zhou, Jia; Chen, Yinbing; Hu, Xiaoping

doi:10.3390/agronomy13010242

Cited by 17 publications

(12 citation statements)

References 28 publications

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“…Secondly, wheat ear FHB detection at ear level directly classifies the severity of FHB severities using a classification network based on segmented or detected wheat ears. Mao et al [31] proposed a lightweight model with an average accuracy of up to 99.23% based on individual wheat ears with disturb of various complex backgrounds inside and outside. However, compared with other technical roadmaps, the analysis at the ear level makes more technical demands of data acquisition or preprocessing.…”

Section: Detection Based On Rgb Imagingmentioning

confidence: 99%

Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective

Feng,

Gu,

Wang

et al. 2024

Plants

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Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechanisms and changes in the characteristics of infected wheat. On this basis, the imaging scales are divided into microscopic, medium, submacroscopic, and macroscopic scales. Then, we outline the recent relevant articles, algorithms, and methodologies about wheat FHB from disease detection to qualitative analysis and summarize the potential difficulties in the practicalization of the corresponding technology. This paper could provide researchers with more targeted technical support and breakthrough directions. Additionally, this paper provides an overview of the ideal application mode of the FHB detection technologies based on multi-scale imaging and then examines the development trend of the all-scale detection system, which paved the way for the fusion of non-destructive detection technologies of wheat FHB based on multi-scale imaging.

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Section: Detection Based On Rgb Imagingmentioning

confidence: 99%

Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective

Feng,

Gu,

Wang

et al. 2024

Plants

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“…Accurate severity assessment of FHB is crucial for timely control of the disease. Mao et al [7] contributed an article on qualitative identification of the severity of FHB by using deep learning. The authors constructed an FHB severity grading dataset (FHBSGD) by using 2752 images in five severity levels of FHB acquired under the experimental and complex field conditions.…”

Section: Disease Image Segmentation and Image-based Disease Identific...mentioning

confidence: 99%

Epidemiology and Control of Fungal Diseases in Crop Plants

Wang

2023

Agronomy

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Crop fungal diseases are a major threat to crop health and food security worldwide. The epidemiology is the basis for effective and sustainable control of crop fungal diseases. Safe, effective, sustainable, and eco-friendly disease control measures have important economic, ecological, and social significances. This Special Issue, “Epidemiology and Control of Fungal Diseases of Crop Plants”, collected one communication and nine original research articles focusing on the identification and detection of the causal agents of alfalfa Fusarium root rot, strawberry black spot, and barley leaf stripe; the semantic segmentation of wheat stripe rust images; the image-based identification of wheat stripe rust and wheat leaf rust; the image-based identification of the severity of wheat Fusarium head blight; the development process of vanilla Fusarium wilt; the regional migration of wheat leaf rust pathogen; the early prediction of potato early blight; the screening of alternative fungicides for the control of alfalfa Fusarium root rot; and the biocontrol potential of endophytic fungi to control of cumin root rot, presenting the progress of research on the epidemiology and control of crop fungal diseases. The studies contained in this Special Issue facilitated the development of epidemiology of the related crop fungal diseases and provided some basis for control of the diseases, which is conducive to the sustainable management of these diseases.

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“…The parame ter-free attention mechanism is simple and efficient. Most of the operators are selected based on the energy function; no additional adjustments to the internal network structure are required [35]. The features with full 3D weights are shown in Figure 3.…”

Section: Simple and Efficient Parameter-free Attention Mechanismmentioning

confidence: 99%

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Liu,

Huang,

Duan

et al. 2023

Electronics

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Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

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GSEYOLOX-s: An Improved Lightweight Network for Identifying the Severity of Wheat Fusarium Head Blight

Cited by 17 publications

References 28 publications

Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective

Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective

Epidemiology and Control of Fungal Diseases in Crop Plants

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

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