Image‐based crop row detection utilizing the Hough transform and DBSCAN clustering analysis

In today’s digital trend, deep learning technology has become an indispensable innovation power in the field of graphic design. In order to better integrate traditional visual elements from graphic design into graphic design, this paper proposes a graphic design layout generation model based on the LayoutGAN model. Taking Chinese traditional paper-cutting art as an example, we construct a dataset of visual elements and use Gaussian filtering, the LGE model, and the OTSU algorithm to preprocess the images of visual elements in paper-cutting art. The LayoutGAN model utilizes the layout element wireframe rendering module to enhance the extraction of visual elements of traditional art and a 2-stage training strategy is employed for model optimization training. To demonstrate the feasibility of applying the model in the reconstruction of visual elements of graphic design art, it was tested experimentally. After the LGE model was used for the image enhancement of the visual elements of paper-cutting art, the image enhancement effect was improved by 30.68%, and the segmentation accuracy and the mIoU of the traditional art visual elements were 98.24% and 97.58%, respectively. The PSNR value of the graphic design generated using the LayoutGAN model is 21.27, and the subjective evaluation scores are all above 3. The reconstruction of traditional visual elements in graphic design can be achieved using deep learning models, and the original information is preserved to the maximum extent.

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Key Technologies of Intelligent Weeding for Vegetables: A Review

Jiao,

Zang,

Chen

2024

Agriculture

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Vegetables are an essential part of people’s daily diet, and weeds can cause serious losses in vegetable yield and quality. Intelligent weeding technology for vegetables will be one of the mainstream technologies in modern agricultural development. This article reviews the current research status of intelligent weeding technology for vegetables, including vegetable and weed detection technology, weeding actuators, and weeding robots. Firstly, the vegetable and weed detection technology was introduced in detail from three aspects: global weed detection, crop-rows detection, and vegetable/weed precise recognition technology. The research results of some researchers were summarised, and the vegetable/weed precise recognition technology, including machine learning and proximal sensor technology, was introduced. Secondly, the weeding actuators and robots were introduced, including intelligent chemical weeding, mechanical weeding, physical weeding, and integrated weed management methods. Some weeding actuators and robots developed by researchers and agricultural companies were showcased. Finally, the challenges and future development directions of intelligent weeding technology were discussed and analysed. Intelligent weeding technology for vegetables is still mainly limited by natural conditions and a lack of technology. In the future, it will be possible to develop in the direction of multi-algorithm and multi-sensor fusion technologies. It is necessary to improve the applicability of intelligent weeding equipment for various environments, crops, and weeds. This article can provide a reference for future research in the field of intelligent weeding for vegetables.

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Image‐based crop row detection utilizing the Hough transform and DBSCAN clustering analysis

Cited by 5 publications

References 68 publications

Paddy Rows Detection in "Legowo" Paddy Field Based on YOLO and Hough Transform

Paddy Rows Detection in "Legowo" Paddy Field Based on YOLO and Hough Transform

Extraction and Reconstruction of Traditional Art Visual Elements by Graphic Design Incorporating Deep Learning

Key Technologies of Intelligent Weeding for Vegetables: A Review

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