The sector is also facing increased demands from the availability of labor to inefficient resource utilization.

This project presents a self-guiding IoT-powered robot for irrigating crops. It utilizes with help the mobile

robot frame and its control via the Arduino framework. The decision-making mechanism in the system

enables automated irrigation based on soil condition, and the ultrasonic sensors provide collision detection

predict plant health and GPS for navigation.

Soil Moisture Monitoring, Autonomous Navigation, Embedded Systems

Agriculture continues to be a crucial element in many regions due to the significant role it plays in people's

daily activities. In regions where agriculture accounts for the majority of the population's income, the use of

old approaches remains prominent despite their high labor input, wastage of water resources, inefficiencies in

assessment and prediction of crops' conditions, and increase in operational costs associated with changing

weather and climate conditions. Traditional irrigation techniques and crop evaluation techniques are water-

consuming, lack accuracy in evaluating crops' health, and increase costs.

The integration of automation tools contributes to conserving resources, especially water, which makes the

solution sustainable.

The appearance of automated agricultural robots represents another milestone reached by modern technology.

Autonomous mobile robots provide a unique combination of mobility, sensing, and actuating capabilities

necessary for gathering data, conducting certain actions, and performing navigation tasks without operator

intervention. An integrated autonomous navigation capability makes the operation of the robot easier since it

can perform actions on its own. A wireless module remotely monitor the obtained Despite such promising

advancements made by modern technology in the field, many of existing systems are rather expensive,

inefficient, and non-integrated. Some of the current solutions concentrate exclusively on monitoring and

irrigation, thus lacking mobility capabilities. Others feature complicated architecture that is hard for small and

Fig-1 System overview of automatic agriculture robot

With the increasing need for intelligent and precise farming, automation has become increasingly popular and

widely discussed in relation to agriculture. By merging sensing technologies, robotics, and powerful

communication systems, the scientific community managed to come up with an innovative solution that is

expected to improve productivity while reducing the amount of labor. Numerous researchers have proposed

monitoring, and reduced human labor. All of these inventions serve as the foundation for smart agriculture;

however, there are a number of limitations to them.

Zhang et al. (2017) developed an autonomous agricultural robot that could be used for precise agricultural

practices. As found by the authors, the robot was able to perform activities such as seeding, and crop

monitoring rather efficiently. Despite this, the robot had to rely on complicated infrastructure and

sophisticated controls, which made it rather difficult to be applied in agriculture because of high costs.

Nandurkar et al. (2014) suggested using wireless sensor networks to implement a precision agriculture

framework for automation of irrigation control. By regulating irrigation according to soil moisture readings,

it was possible to decrease the amount of wasted water and maximize the effectiveness of resources usage.

Even though the technology proved itself to be successful in providing automation, it was unable to perform

with the help of sensing and path planning algorithms. The robot was effective at navigating the area and

detecting obstacles. Nonetheless, the robot was incapable of measuring environmental parameters or irrigation

control since it was focused solely on navigation.

Hossainused technologies in order to develop a smart agriculture framework. Thanks to IoT

could provide environmental variables, allowing to access collected data from any

place. Even though it provided better situational awareness for farmers, it lacked a robot-like platform, which

made it unable to actively engage in any actions like irrigation.

Furthermore, described algorithms

implemented smart to improve production, detect diseases, and assist in making better decisions about

management. Machine learning was successfully applied for this purpose, yet there are still some challenges

considered economically feasible for smaller farms due to high costs.

Recent advancements in embedded systems along with IoT technologies have allowed designing new

many cheap and powerful microcontrollers and sensor modules available for design; however, most of these

systems do not integrate with each other and can function independently without collaboration.

After reviewing related works, one may conclude that current solutions focus only on specific tasks that can

be solved with these technologies. At the same time, there is a lack of an integrated system capable of

which is why an automatic agriculture robot is needed.

interactions between hardware modules will run seamlessly. As the core of the whole platform, the main

controller used is the Arduino Uno microcontroller. It is responsible for gathering data from the sensors,

implementing the algorithm needed to regulate the work of the robot, and communicating with other

subsystems of the robot.

soil moisture sensor and the DHT11 atmospheric sensor. Both sensors gather real-time data on certain

parameters of the environmental conditions, such as soil water content and temperature-humidity readings.

All collected data can then be used to make decisions regarding the amount and frequency of irrigation and

while the L298N motor driver helps to ensure smooth and accurate movement.

Fig-2: Block Diagram of Automatic Agriculture Robot

The system uses low-cost and energy-efficient components suitable for real-time agricultural deployment.

Table 2: Component Specifications

Decision-Making Model

Where the threshold value (T) is experimentally set between 30% and 60% depending on crop requirements.

The process works within an endless cycle that involves the following steps:

Activate sensors and communication devices.

Measure the soil moisture level in comparison with the selected one.

Control the watering process if necessary.

Monitor obstacle distance by using an ultrasonic sensor.

Guide the movement of the robot depending on information received.

Transmit collected data via the Wi-Fi device.

The experiment was conducted in an agricultural testing field having dimensions of around 5×5 meters. Soil

moisture was prepared in different conditions within the testing field to replicate actual situations. A robot was

used in the field to carry out the irrigation and sensing operations.

and it was found that the 40% soil moisture level gave optimum results for controlling irrigation.

research is needed to these challenges and improve the Regarding the design, it should be noted that the use

of inexpensive hardware, such as an Arduino controller and conventional sensors, helped reduce costs

without compromising performance. However, certain limitations exist, such as the dependence on Wi-Fi

communications and limited scalability of the technology. Future overcome robot's ability tonavigate

in open spaces.

Future works may include expanding the capabilities of the system to control irrigation in large areas, using

GPS navigation and advanced machine learning algorithms to analyze the condition of crops. Also, it may be

reasonable to consider additional options for collecting renewable energy, such as solar panels.

principle: once the soil moisture falls below 40%, irrigation begins.