Intelligent detection of Multi-Class pitaya fruits in target picking row based on WGB-YOLO network

Nan, Yulong; Zhang, Huichun; Zeng, Yong; Zheng, Jiaqiang; Ge, Yufeng

doi:10.1016/j.compag.2023.107780

Cited by 38 publications

(6 citation statements)

References 34 publications

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“…Therefore, many researchers have explored methods for the intelligent recognition of wild animals by using image-processing technologies [15][16][17][18][19]. Especially in the last decade, deep learning has rapidly evolved and has been widely used in various fields, such as farm produce detection [20,21], equipment fault diagnosis [22,23], animal monitoring [24,25], etc. In wildlife monitoring, most research based on deep learning has focused on species recognition [26][27][28].…”

Section: Related Workmentioning

confidence: 99%

MammalClub: An Annotated Wild Mammal Dataset for Species Recognition, Individual Identification, and Behavior Recognition

Lu,

Zhao,

Wang

et al. 2023

Electronics

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Mammals play an important role in conserving species diversity and maintaining ecological balance, so research on mammal species composition, individual identification, and behavioral analysis is of great significance for optimizing the ecological environment. Due to their great capabilities for feature extraction, deep learning networks have gradually been applied to wildlife monitoring. However, training a network requires a large number of animal image samples. Although a few wildlife datasets contain many mammals, most mammal images in these datasets are not annotated. In particular, selecting mammalian images from vast and comprehensive datasets is still a time-consuming task. Therefore, there is currently a lack of specialized datasets of images of wild mammals. To address these limitations, this article created a mammal image dataset (named MammalClub), which contains three sub-datasets (i.e., a species recognition sub-dataset, an individual identification sub-dataset, and a behavior recognition sub-dataset). This study labeled the bounding boxes of the images used for species recognition and the coordinates of the mammals’ skeletal joints for behavior recognition. This study also captured images of each individual from different points of view for individual mammal identification. This study explored novel intelligent animal recognition models and compared and analyzed them with the mainstream models in order to test the dataset.

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Section: Related Workmentioning

confidence: 99%

MammalClub: An Annotated Wild Mammal Dataset for Species Recognition, Individual Identification, and Behavior Recognition

Lu,

Zhao,

Wang

et al. 2023

Electronics

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“…In Eq. ( 1), X represents the input feature map, where X (c,h,w) denotes the feature input with dimensions of C × H × W. W1 and W2 represent the computed weights; p1 and p2 represent adjustable learning parameters; b signifies the adaptive function; and d represents the sigmoid activation function (Nan et al, 2023). MetaAconC (Ma et al, 2021) is a novel activation function proposed in 2021 to address the limitations of conventional activation functions.…”

Section: Acmix: Attention-based Convolutional Hybrid Structurementioning

confidence: 99%

Rapid and accurate detection of multi-target walnut appearance quality based on the lightweight improved YOLOv5s_AMM model

Zhan,

Li,

Lin

et al. 2023

Front. Plant Sci.

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IntroductionNut quality detection is of paramount importance in primary nut processing. When striving to maintain the imperatives of rapid, efficient, and accurate detection, the precision of identifying small-sized nuts can be substantially compromised.MethodsWe introduced an optimized iteration of the YOLOv5s model designed to swiftly and precisely identify both good and bad walnut nuts across multiple targets. The M3-Net network, which is a replacement for the original C3 network in MobileNetV3’s YOLOv5s, reduces the weight of the model. We explored the impact of incorporating the attention mechanism at various positions to enhance model performance. Furthermore, we introduced an attentional convolutional adaptive fusion module (Acmix) within the spatial pyramid pooling layer to improve feature extraction. In addition, we replaced the SiLU activation function in the original Conv module with MetaAconC from the CBM module to enhance feature detection in walnut images across different scales.ResultsIn comparative trials, the YOLOv5s_AMM model surpassed the standard detection networks, exhibiting an average detection accuracy (mAP) of 80.78%, an increase of 1.81%, while reducing the model size to 20.9 MB (a compression of 22.88%) and achieving a detection speed of 40.42 frames per second. In multi-target walnut detection across various scales, the enhanced model consistently outperformed its predecessor in terms of accuracy, model size, and detection speed. It notably improves the ability to detect multi-target walnut situations, both large and small, while maintaining the accuracy and efficiency.DiscussionThe results underscored the superiority of the YOLOv5s_AMM model, which achieved the highest average detection accuracy (mAP) of 80.78%, while boasting the smallest model size at 20.9 MB and the highest frame rate of 40.42 FPS. Our optimized network excels in the rapid, efficient, and accurate detection of mixed multi-target dry walnut quality, accommodating lightweight edge devices. This research provides valuable insights for the detection of multi-target good and bad walnuts during the walnut processing stage.

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“…proposed a tomato pose detection algorithm based on YOLOv5, aiming to detect the three-dimensional pose of individual tomato fruits, achieving a mAP of 93.4% (Du et al 2023). Nan et aldeveloped WGB-YOLO based on YOLOv7 for harvesting ripe dragon fruits, further classifying them based on different growth poses, achieving a mAP of 86.0% (Nan et al 2023). Therefore, in practical applications, simply classifying harmful tea leaves into one category cannot fully address the issues of robotic harvesting strategy and occlusion.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds

Leng,

Chen,

Huang

et al. 2024

Preprint

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Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, pests and diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses.While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. To address this challenge, this paper introduces the YOLO-DBD network model for detecting of harmful tea leaves. The model excels in efficiently identifying harmful tea leaves with various poses in complex backgrounds, providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning process.The improvements proposed in this study encompass the C2f-DCN module, Bi-Level Routing Attention, Dynamic Head, and Focal-CIoU Loss function, enhancing the model's feature extraction, computation allocation, and perception capabilities. Comparative analysis with the YOLOv8s model demonstrates a 6% improvement in mAP and a reduction of 3.3G FLOPs in the YOLO-DBD model.

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Intelligent detection of Multi-Class pitaya fruits in target picking row based on WGB-YOLO network

Cited by 38 publications

References 34 publications

MammalClub: An Annotated Wild Mammal Dataset for Species Recognition, Individual Identification, and Behavior Recognition

MammalClub: An Annotated Wild Mammal Dataset for Species Recognition, Individual Identification, and Behavior Recognition

Rapid and accurate detection of multi-target walnut appearance quality based on the lightweight improved YOLOv5s_AMM model

An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds

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