Smart Farming Robot for Detecting Environmental Conditions in a Greenhouse

Abstract

Climate change poses significant challenges to agricultural productivity, necessitating the adoption of advanced 
technologies for sustainable crop production. Greenhouse cultivation provides a controlled environment that 
enhances crop growth while minimizing the impact of pests and adverse weather conditions. This paper presents the 
design and implementation of an autonomous greenhouse monitoring robot integrated with Industry 4.0 technologies, 
including the Internet of Things (IoT), wireless sensor networks (WSNs), and machine learning techniques. The robot 
autonomously navigates predefined routes within the greenhouse, collects real-time environmental parameters, and 
transmits data for analysis. An unsupervised clustering algorithm is employed to classify greenhouse regions into 
optimal, standard, and deficient zones, enabling efficient decision-making and resource management. A user-friendly 
monitoring interface is developed to assist farmers in visualizing crop conditions and planning operational routes. 
Furthermore, the robot is designed to operate effectively on uneven terrain through advanced motion control 
mechanisms, ensuring accurate navigation and stability. The proposed system improves greenhouse management, 
enhances crop productivity, and supports the sustainability and profitability of small- and medium-scale agricultural 
enterprises.