Smart Greenhouse

Abstract

Greenhouse farming is a crucial aspect of modern agriculture, enabling crop growth in regulated conditions. Despite this, traditional greenhouse management often depends on manual labor, which can be inefficient and lead to less-than-ideal growth conditions. This study presents an IoT enabled greenhouse automation system that continuously monitors and controls key environmental factors such as temperature, humidity, soil moisture, and light intensity. The system employs cost effective sensors and ESP32 microcontrollers to transmit real-time data to a cloud-based platform for analysis and remote monitoring. This research adopts a mixed-method approach, integrating user feedback with sensor-based quantitative data. The system undergoes iterative development using a prototyping methodology, refining features based on user responses. Additionally, predictive analytics powered by machine learning offer actionable insights, allowing farmers to anticipate and optimize greenhouse conditions for enhanced crop yield. Initial test results demonstrate high accuracy in tracking critical environmental metrics. A confusion matrix and an AUC-ROC score above 0.85 validate the effectiveness of the system in predicting optimal growth conditions. These promising results suggest that this system can improve crop quality, minimize operational costs, and promote sustainability in agriculture through precision farming.