Reasearch on Pear Inflorescence Recognition Based on Fusion Attention Mechanism With Yolov5

Xia, Ye; Lei, Xiaohui; HERBST, Andreas; Lyu, Xiaolan

doi:10.35633/inmateh-69-01

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Wu et al [25] and Tian et al [26] proposed apple flower detection methods based on the YOLOv4 and MASU R-CNN models, respectively, both of which achieved high detection accuracy. Ye et al [27] improved the YOLOv5 model by adding a CA attention mechanism and the feature fusion structure to detect pear flowers. Shang et al [28] used the lightweight YOLO v5s model and added a context extraction module (CAM) to detect apple flowers of various colors under different weather and lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Network for Detection of Peach Blossom Quantity

Sun,

Yao,

Cao

et al. 2024

Agriculture

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In agricultural production, rapid and accurate detection of peach blossom bloom plays a crucial role in yield prediction, and is the foundation for automatic thinning. The currently available manual operation-based detection and counting methods are extremely time-consuming and labor-intensive, and are prone to human error. In response to the above issues, this paper proposes a natural environment peach blossom detection model based on the YOLOv5 model. First, a cascaded network is used to add an output layer specifically for small target detection on the basis of the original three output layers. Second, a combined context extraction module (CAM) and feature refinement module (FSM) are added. Finally, the network clusters and statistically analyzes the range of multi-scale channel elements using the K-means++ algorithm, obtaining candidate box sizes that are suitable for the dataset. A novel bounding box regression loss function (SIoU) is used to fuse the directional information between the real box and the predicted box to improve detection accuracy. The experimental results show that, compared with the original YOLOv5s model, our model has correspondingly improved AP values for identifying three different peach blossom shapes, namely, bud, flower, and falling flower, by 7.8%, 10.1%, and 3.4%, respectively, while the final mAP value for peach blossom recognition increases by 7.1%. Good results are achieved in the detection of peach blossom flowering volume. The proposed model provides an effective method for obtaining more intuitive and accurate data sources during the process of peach yield prediction, and lays a theoretical foundation for the development of thinning robots.

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Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Network for Detection of Peach Blossom Quantity

Sun,

Yao,

Cao

et al. 2024

Agriculture

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REAL-TIME GRAPE DISEASE DETECTION MODEL BASED ON IMPROVED YOLOv8s

REN,

ZHANG,

WANG

et al. 2024

INMATEH

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This research is dedicated to enhancing the accuracy and processing speed of grape disease recognition. As a result, a real-time grape disease detection model named MSCI-YOLOv8s, based on an improved YOLOv8s framework is proposed. The primary innovation of this model lies in replacing the backbone network of the original YOLOv8s with the more efficient MobileNetV3. This alteration not only strengthens the ability of the model to capture features of various disease manifestations in grape leaf images but also improves its generalization capabilities and stability. Additionally, the model incorporates the SPPFCSPC pyramid pooling structure, which maintains the stability of the receptive field while significantly enhancing processing speed. The integration of the CBAM attention mechanism further accentuates the ability of the model to identify key features, substantially increasing the accuracy of disease detection. Moreover, the model employs Inner-SIoU as the loss function, optimizing the precision of bounding box regression and accelerating model convergence, thereby further enhancing detection efficiency. Rigorous testing has shown that the MSCI-YOLOv8s model achieves an impressive average precision (mAP) of 97.7%, with an inference time of just 37.2 milliseconds and a memory footprint of 39.3 MB. These advancements render the MSCI-YOLOv8s not only highly efficient but also extremely practical for real-time grape disease detection, meeting the actual demands of grape orchard disease identification and demonstrating significant potential for application.

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APPLE DETECTION METHOD IN THE NATURAL ENVIRONMENT BASED ON IMPROVED YOLOv5

CHEN,

NIU,

CHENG

et al. 2024

INMATEH

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To improve the accuracy of apple fruit recognition, enhance the efficiency of automatic picking robots in orchards, and provide effective visual guidance for the picking robot, a target recognition network model based on improved YOLOv5 is proposed. Firstly, the original apple images collected and the data images obtained by different data enhancement methods are used to establish a dataset of 1,879 images, and the dataset is divided into the training set and the test set under 8:2; then for the problem of low detection accuracy of apple fruits in the natural environment due to the mutual obstruction of apple fruits, this paper modifies the backbone network of YOLOv5 by adding the attention mechanism of the Transformer module, the Neck structure is changed from the original PAFPN to BiFPN that can perform two-way weighted fusion, and the Head structure adds the P2 module for shallow down sampling; finally, the recognition test is performed on the dataset, and a comparative analysis is performed according to different evaluation indexes to verify the superiority of the proposed model. The experimental results show that: compared with other existing models and the single-structure improved YOLOv5 model, the comprehensive improved model proposed in this paper has higher detection accuracy, resulting in an increase of 3.7% in accuracy.

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Reasearch on Pear Inflorescence Recognition Based on Fusion Attention Mechanism With Yolov5

Cited by 4 publications

References 14 publications

Improved YOLOv5 Network for Detection of Peach Blossom Quantity

Improved YOLOv5 Network for Detection of Peach Blossom Quantity

REAL-TIME GRAPE DISEASE DETECTION MODEL BASED ON IMPROVED YOLOv8s

APPLE DETECTION METHOD IN THE NATURAL ENVIRONMENT BASED ON IMPROVED YOLOv5

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