Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Tamrakar, Niraj; Karki, Sijan; Kang, Myeong Yong; Deb, Nibas Chandra; Arulmozhi, Elanchezhian; Kang, Dae Yeong; Kook, Junghoo; Kim, Hyeon Tae

doi:10.3390/agriengineering6020055

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…In practical applications, due to the deployment needs of detection algorithms on mobile and embedded devices, the development of lightweight and high-precision detection networks has gradually become a research focus. Niraj Tamrakar et al [18] proposed a lightweight strawberry detection and counting algorithm YOLOv5s-CGhostnet based on YOLOv5s. By combining the Ghost module [19] with CBS and C3 modules, the model size and computation are significantly reduced, and the CBAM [20] attention mechanism is introduced to enhance the model's ability to extract strawberry features.…”

Section: Introductionmentioning

confidence: 99%

Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head

Yi,

Chen,

et al. 2024

Agronomy

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Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model’s feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model’s detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management.

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Section: Introductionmentioning

confidence: 99%

Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head

Yi,

Chen,

et al. 2024

Agronomy

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GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios

Li,

Zhang,

2024

Agronomy

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The efficiency of tea bud harvesting has been greatly enhanced, and human labor intensity significantly reduced, through the mechanization and intelligent management of tea plantations. A key challenge for harvesting machinery is ensuring both the freshness of tea buds and the integrity of the tea plants. However, achieving precise harvesting requires complex computational models, which can limit practical deployment. To address the demand for high-precision yet lightweight tea bud detection, this study proposes the GLS-YOLO detection model, based on YOLOv8. The model leverages GhostNetV2 as its backbone network, replacing standard convolutions with depthwise separable convolutions, resulting in substantial reductions in computational load and memory consumption. Additionally, the C2f-LC module is integrated into the improved model, combining cross-covariance fusion with a lightweight contextual attention mechanism to enhance feature recognition and extraction quality. To tackle the challenges posed by varying poses and occlusions of tea buds, Shape-IoU was employed as the loss function to improve the scoring of similarly shaped objects, reducing false positives and false negatives while improving the detection of non-rectangular or irregularly shaped objects. Experimental results demonstrate the model’s superior performance, achieving an AP@0.5 of 90.55%. Compared to the original YOLOv8, the model size was reduced by 38.85%, and the number of parameters decreased by 39.95%. This study presents innovative advances in agricultural robotics by significantly improving the accuracy and efficiency of tea bud harvesting, simplifying the configuration process for harvesting systems, and effectively lowering the technological barriers for real-world applications.

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BHI-YOLO: A Lightweight Instance Segmentation Model for Strawberry Diseases

Hu,

Chen,

Huang

et al. 2024

Applied Sciences

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In complex environments, strawberry disease segmentation models face challenges, such as segmentation difficulties, excessive parameters, and high computational loads, making it difficult for these models to run effectively on devices with limited computational resources. To address the need for efficient running on low-power devices while ensuring effective disease segmentation in complex scenarios, this paper proposes BHI-YOLO, a lightweight instance segmentation model based on YOLOv8n-seg. First, the Universal Inverted Bottleneck (UIB) module is integrated into the backbone network and merged with the C2f module to create the C2f_UIB module; this approach reduces the parameter count while expanding the receptive field. Second, the HS-FPN is introduced to further reduce the parameter count and enhance the model’s ability to fuse features across different levels. Finally, by integrating the Inverted Residual Mobile Block (iRMB) with EMA to design the iRMA, the model is capable of efficiently combining global information to enhance local information. The experimental results demonstrate that the enhanced instance segmentation model for strawberry diseases achieved a mean average precision (mAP@50) of 93%. Compared to YOLOv8, which saw a 2.3% increase in mask mAP, the improved model reduced parameters by 47%, GFLOPs by 20%, and model size by 44.1%, achieving a relatively excellent lightweight effect. This study combines lightweight architecture with enhanced feature fusion, making the model more suitable for deployment on mobile devices, and provides a reference guide for strawberry disease segmentation applications in agricultural environments.

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Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Cited by 3 publications

References 24 publications

Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head

Light-FC-YOLO: A Lightweight Method for Flower Counting Based on Enhanced Feature Fusion with a New Efficient Detection Head

GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios

BHI-YOLO: A Lightweight Instance Segmentation Model for Strawberry Diseases

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