AI-Driven Autonomous Precision Agriculture System for Smart Crop Management

Abstract

The rapid growth of global food demand, coupled with the challenges posed by plant diseases and weed infestation, necessitates the development of intelligent and autonomous agricultural systems. This paper presents a survey of an AI-Driven Autonomous Precision Agriculture System for Smart Crop Management that integrates two core functionalities: (1) an autonomous field robot capable of navigating crop rows, detecting and removing weeds, and recording the entire process in real time, and (2) a deep learning-based crop disease detection system targeting Rice, Onion, Cotton, and Tomato crops with integrated soil temperature and humidity sensing. The proposed system employs a Raspberry Pi-based low-budget rover equipped with a camera module and IoT sensors for real-time data acquisition. Deep learning models including Convolutional Neural Networks (CNN) and transfer learning architectures are used for weed identification and disease classification. The survey reviews existing approaches, identifies critical research gaps such as the absence of multimodal sensor fusion and closed-loop autonomous action, and proposes an integrated framework that addresses these limitations. The system aims to reduce chemical usage, improve crop yield, and support sustainable precision agriculture practices.

Introduction

The text discusses challenges in modern agriculture such as crop diseases, weed infestations, environmental degradation from chemical use, and the limitations of traditional manual farming. It highlights how AI, IoT, robotics, and deep learning—especially CNN-based models—can enable precision agriculture through automated monitoring, disease detection, and weed removal using low-cost platforms like Raspberry Pi.

A key problem identified in existing systems is that most focus on only one aspect (either disease detection or robotics) and often rely only on image data without incorporating environmental factors like soil moisture, temperature, and humidity. Additionally, many systems lack full integration, real-world deployment, or autonomous action capabilities.

The literature review shows various approaches including deep learning models for plant disease detection, IoT-based sensor systems for environmental monitoring, and agricultural robots for spraying or greenhouse management. While these systems achieve good accuracy or efficiency improvements, they often suffer from limitations such as lack of multimodal sensor fusion, absence of robotics, limited crop coverage, or restricted real-world usability.

To address these gaps, the proposed system introduces an integrated precision agriculture framework with two main components: an autonomous field robot for weed detection and removal, and a crop disease detection module for multiple crops (rice, onion, cotton, tomato). The system combines CNN-based image analysis with environmental sensor data (soil moisture, temperature, humidity) to improve prediction accuracy. The robot can navigate fields, detect weeds using lightweight models, physically remove them, and log data to a dashboard or cloud system.

Conclusion

This paper presented a survey and proposed methodology for an AI-Driven Autonomous Precision Agriculture System for Smart Crop Management. The review of existing literature revealed that while individual components such as disease detection models, IoT sensor networks, and agricultural robots have been developed independently, no existing system integrates all three into a unified closed-loop precision agriculture framework. The proposed system uniquely combines a Raspberry Pi-based low-cost autonomous rover for weed detection and removal, a multi-crop disease classification model covering Rice, Onion, Cotton, and Tomato, and a multimodal sensor fusion pipeline incorporating soil temperature and humidity data — directly addressing the limitations identified across all reviewed works. The complete hardware requirements estimated at approximately INR 12,380 and the open-source software stack make this system practically implementable at low cost, ensuring accessibility for smallholder farmers. Future enhancements may include GPS-based field mapping for autonomous multi-row navigation, integration of weather forecast APIs for predictive disease risk assessment, and federated learning for collaborative model improvement across multiple farms. This work contributes to the growing body of research in autonomous precision agriculture and lays a practical foundation for a fully autonomous, affordable, and intelligent crop management system.

References

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Copyright

Copyright © 2026 Prof. Nagaratna C, Akshatha S, Harshitha C N, Hemalatha N L , Likitha V Yadav. 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.