<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Utilizing Internet technology and resources to enhance the packaging design of agricultural products is a critical task for promoting their development in the current era. This study proposes an automatic generation design for agricultural product packaging based on deep learning theory, grounded in the principles of packaging design and market value. To represent the spatial relationship and distribution of color pixels, color moments and color correlation maps are used after obtaining color features of agricultural products through the HSV color space. The grayscale covariance matrix method is employed to get the texture features necessary for the packaging design. Proportion, boundary, pairing, white space, and balance calculations are combined to achieve the layout and typography of feature elements in the agricultural packaging design. The impact of deep learning technology-supported packaging design is analyzed. Data show that in the first group, the average values of the sample group (P001, P002, P004) are 2.98, 3.08, and 2.93, respectively, with an overall average of 2.997, while the overall average of the intelligent group (P003) is 2.96. Overall, deep learning technology-assisted image generation has a positive impact on agricultural product packaging design, contributing to a new level of economic development of farming products.</p>
</abstract>

Utilizing Internet technology and resources to enhance the packaging design of agricultural products is a critical task for promoting their development in the current era. This study proposes an automatic generation design for agricultural product packaging based on deep learning theory, grounded in the principles of packaging design and market value. To represent the spatial relationship and distribution of color pixels, color moments and color correlation maps are used after obtaining color features of agricultural products through the HSV color space. The grayscale covariance matrix method is employed to get the texture features necessary for the packaging design. Proportion, boundary, pairing, white space, and balance calculations are combined to achieve the layout and typography of feature elements in the agricultural packaging design. The impact of deep learning technology-supported packaging design is analyzed. Data show that in the first group, the average values of the sample group (P001, P002, P004) are 2.98, 3.08, and 2.93, respectively, with an overall average of 2.997, while the overall average of the intelligent group (P003) is 2.96. Overall, deep learning technology-assisted image generation has a positive impact on agricultural product packaging design, contributing to a new level of economic development of farming products.

Exploration of Agricultural Product Packaging Design Innovation in the Context of Deep Learning

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Utilizing Internet technology and resources to enhance the packaging design of agricultural products is a critical task for promoting their development in the...

Exploration of Agricultural Product Packaging Design Innovation in the Context of Deep Learning

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 22 kwi 2024

Przyjęty: 06 lip 2024

DOI: https://doi.org/10.2478/amns-2024-1882

Słowa kluczowe
Deep learning technology, HSV color space, Grayscale covariance matrix, Packaging design

© 2024 Songyong Hu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploration of Agricultural Product Packaging Design Innovation in the Context of Deep Learning

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 22 kwi 2024

Przyjęty: 06 lip 2024

DOI: https://doi.org/10.2478/amns-2024-1882

Słowa kluczoweDeep learning technology, HSV color space, Grayscale covariance matrix, Packaging design

© 2024 Songyong Hu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep learning technology, HSV color space, Grayscale covariance matrix, Packaging design