Smart irrigation prediction system for garden plants

Abstract

Water scarcity and inefficient irrigation practices pose significant challenges to sustainable agriculture and gardening. Excessive water usage leads to depletion of resources, while insufficient irrigation hampers plant growth. This research presents a smart irrigation prediction system that utilizes machine learning to optimize water management. By analyzing key environmental factors such as temperature, humidity, sunlight exposure, and soil moisture levels, the system accurately predicts water requirements and evaporation rates. Additionally, it recommends the most efficient irrigation methods tailored to different plant species and environmental conditions. The proposed solution not only conserves water but also enhances plant health by maintaining optimal hydration levels. Implemented as a user-friendly web-based tool, this system empowers gardeners and farmers to make informed irrigation decisions, ultimately contributing to sustainable agriculture and responsible water usage. The integration of artificial intelligence in irrigation planning represents a step forward in addressing global water conservation challenges while ensuring productive and healthy plant growth.