Artificial Intelligence Based Domestic Plant Selection for Optimum Sustainability

Abstract

In the era of environmental awareness and sustainability, the concept of "Go Green" has gained significant momentum. Urban households and indoor gardening enthusiasts often struggle to select suitable plants that can thrive in their specific environmental conditions, leading to poor plant growth and resource wastage. Existing solutions primarily rely on generic plant recommendation systems or manual selection based on experience, which may not ensure optimal sustainability. To address this challenge, this paper presents an Artificial Intelligence-based plant selection system that utilizes real-time environmental data, including temperature, humidity, and soil moisture, collected using sensors. The system employs a Random Forest algorithm to analyze these parameters and match them with a curated plant dataset, ensuring the selection of the most suitable plants for a given location. Experimental results demonstrate that the proposed approach enhances plant survival rates and promotes efficient resource utilization. This AI-driven solution provides an intelligent, automated, and sustainable method for plant selection, contributing to the broader goal of environmental conservation and urban greenery optimization.