Machine Learning–Based Climate-Adaptive Smart Gardening System for Sustainable Urban Agriculture

Urban Farm DB summary

This research proposed and aimed to design and implement a Machine Learning-Based Climate-Adaptive Smart Gardening System for sustainable urban agriculture. The system uses environmental sensors to record soil moisture, air temperature, humidity, solar exposure, and light intensity in real-time. It employs machine learning models like Gradient Boosting, Random Trees, and LSTM to predict irrigation needs, plant stress, and environmental changes, adjusting gardening conditions to mitigate climate impacts.