Autonomous navigation method of jujube catch-and-shake harvesting robot based on convolutional neural networks

Zheng, Zhouzhou; Hu, Yaohua; Li, Xingang; Huang, Yuxiang

doi:10.1016/j.compag.2023.108469

Cited by 7 publications

(1 citation statement)

References 34 publications

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“…Chunmei et al extracted the Otsu feature from the image, then used the Otsu threshold algorithm for automatic threshold segmentation and extracted pixels representing the fruit, with an accuracy rate of over 95% 20 . Zhouzhou et al improved the YOLOX model using techniques such as CSP Attention Block, SPPCSPC-F, and ASFF, resulting in a model named YOLOX-Nano, which achieved an mAP value of 84.08% for positioning 21 . Yuxiang et al proposed a universal attention module (AGHRNet) capable of separating the background from the detected subject, which realized higher segmentation accuracy and smaller model parameters 22 .…”

Section: Related Workmentioning

confidence: 99%

An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece

Chang,

Zhou,

Tang

et al. 2024

Sci Rep

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In recent years, with the increasing demand for high-quality Dendrobii caulis decoction piece, the identification of D. caulis decoction piece species has become an urgent issue. However, the current methods are primarily designed for professional quality control and supervision. Therefore, ordinary consumers should not rely on these methods to assess the quality of products when making purchases. This research proposes a deep learning network called improved YOLOv5 for detecting different types of D. caulis decoction piece from images. In the main architecture of improved YOLOv5, we have designed the C2S module to replace the C3 module in YOLOv5, thereby enhancing the network’s feature extraction capability for dense and small targets. Additionally, we have introduced the Reparameterized Generalized Feature Pyramid Network (RepGFPN) module and Optimal Transport Assignment (OTA) operator to more effectively integrate the high-dimensional and low-dimensional features of the network. Furthermore, a new large-scale dataset of Dendrobium images has been established. Compared to other models with similar computational complexity, improved YOLOv5 achieves the highest detection accuracy, with an average mAP@.05 of 96.5%. It is computationally equivalent to YOLOv5 but surpasses YOLOv5 by 2 percentage points in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece

Chang,

Zhou,

Tang

et al. 2024

Sci Rep

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Cost-efficient algorithm for autonomous cultivators: Implementing template matching with field digital twins for precision agriculture

De Bortoli,

Marsi,

Marinello

et al. 2024

Computers and Electronics in Agriculture

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Grape clusters detection based on multi-scale feature fusion and augmentation

Ma,

Xu,

et al. 2024

Sci Rep

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This paper addresses the challenge of low detection accuracy of grape clusters caused by scale differences, illumination changes, and occlusion in realistic and complex scenes. We propose a multi-scale feature fusion and augmentation YOLOv7 network to enhance the detection accuracy of grape clusters across variable environments. First, we design a Multi-Scale Feature Extraction Module (MSFEM) to enhance feature extraction for small-scale targets. Second, we propose the Receptive Field Augmentation Module (RFAM), which uses dilated convolution to expand the receptive field and enhance the detection accuracy for objects of various scales. Third, we present the Spatial Pyramid Pooling Cross Stage Partial Concatenation Faster (SPPCSPCF) module to fuse multi-scale features, improving accuracy and speeding up model training. Finally, we integrate the Residual Global Attention Mechanism (ResGAM) into the network to better focus on crucial regions and features. Experimental results show that our proposed method achieves a mAP of 93.29% on the GrappoliV2 dataset, an improvement of 5.39% over YOLOv7. Additionally, our method increases Precision, Recall, and F1 score by 2.83%, 3.49%, and 0.07, respectively. Compared to state-of-the-art detection methods, our approach demonstrates superior detection performance and adaptability to various environments for detecting grape clusters.

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Autonomous navigation method of jujube catch-and-shake harvesting robot based on convolutional neural networks

Cited by 7 publications

References 34 publications

An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece

An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece

Cost-efficient algorithm for autonomous cultivators: Implementing template matching with field digital twins for precision agriculture

Grape clusters detection based on multi-scale feature fusion and augmentation

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