AI-Based Agricultural Robot for Real-Time Crop Monitoring: A Comprehensive Review

Abstract

Agriculture is evolving toward intelligent automation due to increasing demand for food production, environmental challenges, and labor shortages. Artificial Intelligence (AI), Internet of Things (IoT), and robotic technologies enable precision farming through continuous monitoring and automated decision-making. This review paper presents a detailed analysis of an AI-based agricultural robot designed for real-time crop monitoring, autonomous navigation, and smart irrigation management. The system integrates environmental sensors, wireless communication, cloud analytics, and automated treatment mechanisms to optimize agricultural productivity while reducing resource wastage. Existing research on agricultural robots, intelligent pesticide spraying systems, and precision farming technologies is critically reviewed. The study highlights the effectiveness of sensor-driven automation in improving crop yield, conserving water, and minimizing chemical usage. Future developments involving computer vision, predictive analytics, and swarm robotics are also discussed.

Introduction

The text describes an AI-based agricultural robot system designed to improve farming efficiency through automation, IoT, and precision agriculture techniques.

It begins by explaining the limitations of traditional farming, such as inefficient irrigation, delayed disease detection, and heavy reliance on manual labor. To overcome these issues, the proposed system uses smart agricultural robots that continuously monitor environmental conditions like soil moisture, temperature, humidity, and light, and use this data to make automated decisions.

The literature review highlights several related works in agricultural robotics, including systems for:

• Automated pesticide spraying based on plant disease detection
• Autonomous greenhouse spraying robots
• Wireless monitoring and embedded control systems
• Vision-based navigation and crop disease detection

These studies show that combining robotics, sensors, and AI improves farming efficiency but still requires more affordable and practical solutions for real-world use.

The proposed system architecture is a cyber-physical IoT-based model that supports both manual and autonomous modes. Its main functions include:

• Environmental monitoring
• Smart irrigation
• Precision pesticide spraying
• Cloud-based analytics

The system works through a pipeline: sensors → ESP32 microcontroller → cloud platform → AI decision-making → actuators (motors, pump, sprayer).

Key hardware components include:

• ESP32 microcontroller for processing and connectivity
• Sensors for soil moisture, temperature/humidity (DHT11), light, and obstacle detection
• Actuation systems like DC motors, pumps, and sprayers
• Solar-powered battery system for sustainability

Software and cloud integration uses:

• Arduino-based firmware
• Platforms like Firebase and ThingSpeak
• Mobile apps for monitoring, alerts, and manual control

A key application is smart irrigation, where water is automatically supplied when soil moisture drops below a threshold, reducing water usage by about 40%.

Conclusion

AI-based agricultural robots represent a transformative advancement in precision agriculture. The integration of IoT sensors, autonomous mobility, and cloud-based intelligence enables efficient monitoring and automated crop management. The reviewed system demonstrates significant improvements in water conservation, productivity, and sustainability. Continued research in AI and robotics will enable scalable autonomous farming solutions capable of addressing global food security challenges.

References

[1] M.G. Sumithra and G.R. Gayathiri, Agricultural Robot for Leaf Disease Detection [2] P.J. Sammons et al., Autonomous Pesticide Spraying Robot for Greenhouses [3] C. Xu et al., Intelligent Control in Pesticide Spraying Simulation [4] S.R. Gengaje and S.M. Deshmukh, ARM-Based Agricultural Robot [5] M. Raval et al., Automation of Agricultural Spraying Robot [6] N. Zhang et al., Precision Farming Worldwide Overview [7] Jinlinlin & Tony, Vision-Guided Agricultural Robot Navigation [8] Z.B. Husin et al., Image Processing for Plant Health Monitoring [9] AI Agricultural Robot for Real-time Crop Monitoring Documentation

Copyright

Copyright © 2026 K Pimpale , Dr. Yuvraj Kanase, Dr. Sunita Mane. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.