Innovative Organic Gardening Using Artificial Intelligence and Machine Learning
A concise academic overview of a system designed to improve plant monitoring, irrigation and disease recognition.
Background
Organic gardening supports environmental sustainability by avoiding synthetic chemicals, protecting biodiversity and maintaining healthy soil. However, successful plant care requires regular observation of soil moisture, temperature, humidity and visible plant health. Traditional monitoring depends heavily on experience and can result in late watering decisions, over-irrigation or delayed recognition of disease.
This project investigates how Artificial Intelligence, Machine Learning and Internet of Things technologies can support small-scale organic gardening. Sensors provide environmental data, predictive models estimate irrigation needs, and computer vision assists with the recognition of visible plant-health problems.
Project Focus
- Real-time environmental monitoring
- Data-driven irrigation recommendations
- Plant disease detection from images
- Improved water efficiency
- A clear web interface for gardeners
Problem Statement
Manual monitoring may lead to inefficient watering, inconsistent records and late disease detection. Many commercial smart-farming systems are also too expensive or complex for home gardeners and educational environments.
Project Aim
To design an intelligent organic gardening system that improves environmental monitoring, optimises irrigation, supports plant disease detection and promotes sustainable growing practices.
Objectives
Analyse Challenges
Identify problems involving irrigation, soil health and disease recognition.
Collect Data
Use sensors for soil moisture, temperature and humidity readings.
Apply Machine Learning
Generate watering and plant-care recommendations.
Analyse Images
Support disease and nutrient-deficiency recognition.
Develop the Website
Present data and recommendations clearly.
Evaluate the System
Assess accuracy, usability and sustainability benefits.
Scope and Significance
The study focuses on a prototype for home gardens, small-scale growers and educational settings. It excludes large commercial farms, robots, drones and satellite monitoring. Its significance lies in combining sensing, predictive analytics and image recognition within one affordable and understandable platform.