AgriNexa AI with IoT Smart Kisan Sathi

Abstract

Smart agriculture has emerged as an important research area due to increasing challenges related to water scarcity, crop diseases, climate change, and food security. Recent advancements in Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and wireless sensor networks have enabled the development of intelligent farming systems for real-time monitoring and automation. This survey paper presents a comprehensive review of modern smart agriculture technologies, focusing on IoT-based environmental monitoring, AI-driven crop disease detection, automated irrigation systems, cloud-integrated farming platforms, and wireless communication techniques. The paper analyzes various existing research works, technologies, architectures, and methodologies proposed for precision agriculture applications. Comparative analysis of different smart farming approaches is presented based on parameters such as water efficiency, disease detection accuracy, automation capability, scalability, and computational complexity. Furthermore, the paper discusses current challenges, limitations, and future research directions in smart agriculture systems. The survey highlights the growing importance of intelligent and sustainable agricultural solutions for improving productivity, resource optimization, and modern farming practices.

Introduction

Agriculture is a cornerstone of global food production and economic development, but traditional farming methods often suffer from inefficient irrigation, manual monitoring, delayed disease detection, and excessive resource consumption. Recent advances in the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and wireless sensor networks have enabled the development of smart agriculture systems that support real-time monitoring, automated irrigation, and intelligent crop management. This survey reviews research published between 2014 and 2025 on IoT- and AI-based smart farming technologies, focusing on system architectures, applications, advantages, limitations, and future research directions.

The survey categorizes existing research into key technological domains, including IoT-based monitoring, AI and deep learning, automated irrigation, cloud computing, and wireless communication technologies such as Wi-Fi, LoRaWAN, GSM, NB-IoT, and 5G. IoT systems use sensors for soil moisture, temperature, humidity, pH, and light intensity, integrated with controllers like ESP32, Arduino, and Raspberry Pi, while cloud platforms such as Firebase, AWS IoT, Microsoft Azure, Google Cloud, and ThingSpeak enable centralized data storage and remote monitoring. AI techniques, particularly Convolutional Neural Networks (CNNs), are widely applied for crop disease detection, yield prediction, pest identification, weather forecasting, and fertilizer recommendations, improving decision-making and reducing manual effort.

The review highlights that smart irrigation systems significantly reduce water wastage by automatically controlling irrigation based on soil moisture levels, while AI-based crop disease detection provides faster and more accurate diagnosis than traditional manual inspection. Cloud computing enhances scalability and accessibility by enabling real-time synchronization and remote supervision of agricultural data. Wireless communication technologies play a vital role in connecting sensors, controllers, cloud services, and user applications, with each technology offering different trade-offs in terms of range, power consumption, bandwidth, and deployment suitability.

A comparative analysis of existing systems reveals that although significant progress has been made, most solutions address only specific agricultural challenges, such as irrigation or disease detection, rather than providing fully integrated intelligent farming platforms. Common limitations include high computational requirements, dependence on stable internet connectivity, limited scalability, energy consumption, sensor calibration issues, and data security concerns. Research gaps also include the lack of unified systems combining IoT sensing, AI-based analytics, cloud computing, and automated control for cost-effective rural deployment.

The survey identifies emerging trends such as Edge AI, autonomous drones, blockchain-enabled agricultural supply chains, robotic farming, digital twins, predictive analytics, and sustainable IoT architectures. Future research is expected to focus on integrating these technologies with solar-powered IoT devices, AI-driven predictive systems, smart fertilizer recommendations, autonomous agricultural robots, and real-time weather forecasting to improve agricultural productivity, sustainability, and resource optimization while supporting next-generation precision farming.

Conclusion

This survey paper reviewed recent advancements in IoT and AI-based smart agriculture systems. Various technologies including wireless sensor networks, cloud computing, automated irrigation systems, and AI-based disease detection models were analyzed. The survey highlighted the importance of intelligent farming technologies in improving agricultural productivity, reducing water wastage, enabling automation, and supporting sustainable farming practices. Although smart agriculture systems provide significant benefits, challenges related to scalability, connectivity, energy efficiency, and data security still require further research and development.

References

[1] L. Ruiz-Garcia et al., “Wireless Sensor Technologies in Agriculture,” Sensors, 2009. [2] P. Sharma et al., “IoT-Based Smart Irrigation System,” ICCCA, 2018. [3] A. Kumar and M. Singh, “WSN-Based Irrigation System,” IJERT, 2017. [4] S. P. Mohanty et al., “Plant Disease Detection Using Deep Learning,” Frontiers in Plant Science, 2016. [5] P. P. Ray et al., “IoT-Based Monitoring Systems,” IEEE Access, 2017. [6] M. Ayaz et al., “IoT-Based Smart Agriculture Review,” Future Generation Computer Systems, 2019. [7] J. Gutierrez, J. F. Villa-Medina, A. Nieto-Garibay, and M. Á. Porta-Gándara, “Automated Irrigation System Using a Wireless Sensor Network and GPRS Module,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 1, pp. 166–176, 2014. [8] H. J. Pandya and V. V. Gohil, “Smart Farming System Using IoT and Cloud Computing,” International Journal of Engineering Research & Technology (IJERT), vol. 8, no. 6, pp. 1–5, 2019.

Copyright

Copyright © 2026 Manas Gaikwad, Pranav Bhosale, Pratik Sakhare, Anant Rangdal, Prof. Geetanjali Bansod. 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.