IMPROVED YOLOv8-BASED AUTOMATED DETECTION OF WHEAT LEAF DISEASES

MA, Na; LI, Yanwen; XU, Miao; YAN, Hongwen

doi:10.35633/inmateh-71-43

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The improved YOLOv7 algorithm achieved an accuracy rate of 96.7% on the test set. Ma et al (2023) proposed an improved YOLOv8 algorithm for the detection of wheat stripe rust, leaf rust, and powdery mildew. The experimental results indicate that the mAP of the improved YOLOv8 model for detecting the three wheat leaf diseases is 98.8%.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Yang,

Sun,

Wang

et al. 2024

PeerJ Computer Science

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Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.

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Section: Introductionmentioning

confidence: 99%

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Yang,

Sun,

Wang

et al. 2024

PeerJ Computer Science

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Accurate Non-Destructive Testing Method for Potato Sprouts Focusing on Deformable Attention

GENG,

DAI,

ZHANG

et al. 2024

INMATEH

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Accurate potato sprout detection is the key to automatic seed potato cutting, which is important for potato quality and yield. In this paper, a lightweight DAS-YOLOv8 model is proposed for the potato sprout detection task. By embedding DAS deformable attention in the feature extraction network and the feature fusion network, the global feature context can be efficiently represented and the attention increased to the relevant pixel image region; then, the C2f_Atten module fusing Shuffle attention is designed based on the C2f module to satisfy the attention to the key feature information of the high-level abstract semantics of the feature extraction network. At the same time, the ghost convolution is introduced to improve the C2f module and convolutional module to realize the decomposition of the redundant features to extract the key features. Verified on the collected potato sprout image data set, the average accuracy of the proposed DAS-YOLOv8 model is 94.25%, and the calculation amount is only 7.66 G. Compared with the YOLOv8n model, the accuracy is 2.13% higher, and the average accuracy is 1.55% higher. In comparison to advanced state-of-the-art (SOTA) target detection algorithms, the method in this paper offers a better balance between comprehensive performance and lightweight model design. The improved and optimized DAS-YOLOv8 model can realize the effective detection of potato sprouts, meet the requirements of real-time processing, and can provide theoretical support for the non-destructive detection of sprouts in automatic seed potato cutting.

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Research on Locust Target Detection Algorithm Based on Yolo V7 Mobilenetv3-Ca

QIU

2024

INMATEH

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To accurately detect various kinds of locusts in real-time and make locust detection more universal, A locust data set that contains all kinds of locusts was created through the Internet crawler and public dataset IP102, and a locust target detection algorithm YOLOv7-MobileNetV3-CA.was proposed in this paper, Firstly, to reduce the size of model parameters, the backbone of YOLOv7 was replaced by MobileNetV3, Secondly, a CA (Coordinate Attention) attention mechanism was added to further improve the detection accuracy of locusts. after feature enhancement. The experiment showed that the precision of locusts was 95.96%, the recall rate was 92%, the AP was 95.74%, and the F1 was 0.92. Compared with YOLOv7, the model size was reduced by 27%, and the AP was improved by 4.48%. Compared with YOLOv4, YOLOv4 MobileNetV3, YOLOv5, and SSD algorithms, AP has improved by 51.16%, 26.81%, 11.9%, and 11.75%, respectively. Experiments have shown that this algorithm performs well in detecting locusts of different scales, scenes, and types, and can provide reference for real-time locust detection.

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IMPROVED YOLOv8-BASED AUTOMATED DETECTION OF WHEAT LEAF DISEASES

Cited by 3 publications

References 21 publications

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

YOLOv8s-CGF: a lightweight model for wheat ear Fusarium head blight detection

Accurate Non-Destructive Testing Method for Potato Sprouts Focusing on Deformable Attention

Research on Locust Target Detection Algorithm Based on Yolo V7 Mobilenetv3-Ca

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