A Deep Learning Flower Generation System for Home Gardens

Abstract

"In our increasingly urbanized world, the demand for garden design has risen in tandem with the growth of urban populations. Garden design serves the primary purpose of enhancing the aesthetic appeal of urban living spaces while fostering a profound connection between individuals and the natural world. When embarking on the creation of a home garden, especially one focused on a diverse range of beautiful flower colors, several critical factors come into play.

First and foremost, meticulous attention must be paid to the garden's layout and the selection of flower varieties that align with the intended design. Moreover, the color scheme within the garden should complement the existing colors of the home, ensuring a cohesive and visually pleasing overall aesthetic. Flowers offer an extensive array of sizes, colors, and bloom times, making informed decisions vital to avoid disappointment and frustration.

However, in Sri Lanka, the absence of a comprehensive flower generation system compounds these challenges. The limited availability of information about suitable flowers for the local climate, coupled with language barriers and limited awareness of flower varieties, poses significant obstacles to garden owners.

To address this issue, the author has developed the GrowFlora system for Sri Lankan flowers, complete with detailed cultivation instructions. This innovative system allows garden owners to input textual descriptions of their desired flower characteristics. By utilizing advanced generative algorithms, the GrowFlora system generates corresponding flower images based on these descriptions. Additionally, it incorporates color preferences, enabling users to visualize potential color schemes for their home gardens, thus empowering them to make well-informed decisions about flower choices. This system bridges the gap between textual descriptions and visual representations, providing a practical solution for garden design challenges in Sri Lanka"