SmartAgro

Abstract

Agriculture is a key contributor to economic growth and food security, yet farmers continue to face problems such as climate uncertainty, crop diseases, inefficient irrigation, and limited access to expert advice. Recent developments in Artificial Intelligence (AI) and web technologies make it possible to support farmers through intelligent digital platforms. This review paper presents SmartAgro, an AI?based web application that provides crop recommendations, disease identification support, irrigation guidance, and weather?based advisories. The proposed system focuses on a low?cost, website?only approach that avoids the use of expensive hardware. SmartAgro aims to enhance productivity, sustainability, and ease of access for farmers, particularly small and marginal farmers.

Introduction

Agriculture is vital to economic development, but traditional farming methods based on experience and manual observation are increasingly inadequate due to challenges such as climate change, unpredictable weather, soil degradation, and crop diseases. Artificial Intelligence (AI) offers data-driven solutions by analyzing soil, weather, and crop data to support informed farming decisions. In this context, SmartAgro is proposed as a low-cost, AI-powered, web-based agricultural support system that provides essential guidance without requiring expensive hardware.

The primary objective of SmartAgro is to assist farmers through intelligent recommendations, including crop selection based on soil and climate conditions, early crop disease identification, weather-based advisory, and irrigation planning. The system aims to promote efficient resource use, sustainable farming practices, and improved agricultural productivity.

SmartAgro operates by collecting user inputs such as soil type, location, season, and crop preference through a website, along with external weather data. This information is pre-processed and analyzed using AI and machine learning algorithms to generate actionable insights, which are presented in a simple, user-friendly format.

Key features of the system include crop recommendation, disease identification using image-based analysis, weather advisory, irrigation guidance, and practical farming tips. Compared to existing smart agriculture solutions that rely heavily on costly IoT devices and sensors, SmartAgro emphasizes affordability and accessibility by leveraging web technologies and publicly available data.

Despite its advantages, the system has limitations such as dependence on internet connectivity, potential inaccuracies due to limited or outdated datasets, and the impact of incorrect user inputs. Nonetheless, SmartAgro provides a scalable and eco-friendly solution suitable for farmers of all scales. Future enhancements include mobile app development, voice-based assistance, IoT integration, and multilingual support to expand usability and reach.

Overall, SmartAgro demonstrates how AI-driven, web-based platforms can modernize agriculture by making intelligent decision support accessible, sustainable, and cost-effective.

Conclusion

SmartAgro highlights the potential of AI and web technologies in supporting modern agriculture. By offering crop recommendations, disease identification support, irrigation guidance, and weather advisories through a low?cost web?based platform, the system helps farmers make informed decisions and reduce agricultural risks. The website?only approach ensures affordability and accessibility, making SmartAgro a practical solution for promoting sustainable and efficient farming practices.

References

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Copyright

Copyright © 2026 Lect. Manoj Damahe, Vikram Nandanwar, Mayur Chaudhary, Harshal Vyawhare, Tejas Kothalkar, Parth Jadhav. 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.