Bloom Finder: A Smart Flower Recommendation System Using Machine Learning

Authors:

Name of Project Guide:

Mr.S.W.Thakare

(sunny.thakare21241@paruluniversity.ac.in)

(Asst. Prof. Parul University, Vadodara, Gujarat)

Members of Project:

Gunjan Chaudhari

2203051250005@paruluniversity.ac.in

(B.Tech CSE, Parul University)

Abstract

The Flower Recommendation System represents an intelligent decision-support framework designed to assist users in selecting suitable flower species based on environmental and personal preferences. Unlike traditional systems that primarily focus on initial recommendations, this enhanced approach extends functionality by integrating personalized flower care tips, optimized fertilizer guidance, and an expanded range of flower categories. By leveraging Internet of Things (IoT)-enabled sensors in combination with advanced machine learning (ML) algorithms, the system provides precise, data-driven recommendations.

Key environmental parameters such as soil pH, moisture, rainfall, temperature, humidity, and light intensity are continuously monitored, enabling real-time adaptation.

Furthermore, the inclusion of descriptive insights—such as flower characteristics, blooming cycles, and ornamental value—enriches the user experience. This paper highlights the importance of comprehensive flower management that extends beyond selection, promoting biodiversity, sustainability, and improved cultivation practices. The system contributes to the domains of urban agriculture, environmental restoration, and education while providing commercial and ecological value.