Smart Krishi Assistant: An AI-Powered Agricultural Support System

Abstract

Agriculture is a critical sector that supports the livelihood of millions of people, especially in developing countries like India. However, farmers often face challenges such as crop diseases, unpredictable weather conditions, and lack of timely guidance, leading to reduced productivity and financial losses. The Smart Krishi Assistant is an AI-powered agricultural support system designed to address these challenges by integrating crop recommendation, plant disease detection, real-time weather forecasting, and AI-based assistance into a single platform. The system utilizes machine learning models to recommend suitable crops based on soil and environmental parameters. It employs deep learning-based image classification techniques for plant disease detection and integrates external APIs for real-time weather data. Additionally, an AI-powered assistant using Gemini provides natural language-based agricultural guidance. The platform is built using React for the frontend, FastAPI for the backend, and AWS services for deployment and data storage. By combining multiple intelligent features into a unified system, the Smart Krishi Assistant enhances decision-making, improves productivity, and provides accessible technological support to farmers. The system aims to bridge the gap between modern AI technologies and traditional farming practices, enabling smarter and more efficient agriculture.

Introduction

Agriculture is essential for economic growth and food security, but traditional farming faces challenges such as crop diseases, lack of accurate weather data, and limited expert guidance. Existing technological solutions often address only single problems and require costly infrastructure, making them less accessible to farmers.

To overcome these issues, the Smart Krishi Assistant is proposed as an AI-powered, integrated agricultural platform. It combines multiple functionalities into one system, enabling farmers to:

• Detect crop diseases using image-based deep learning
• Receive real-time weather updates
• Get crop recommendations based on environmental conditions
• Interact with an AI assistant for instant guidance

The system follows a client-server architecture, with a React-based frontend and a FastAPI backend, integrating AI models, external APIs, and a DynamoDB database. It is designed to be modular, scalable, and user-friendly, allowing seamless interaction across different services.

The development process includes problem identification, system design, frontend and backend implementation, AI integration, and deployment using cloud platforms like AWS and Vercel.

Results show that the system is easy to use and effective, with the disease detection module achieving high accuracy in identifying plant diseases. Overall, the Smart Krishi Assistant provides a centralized, efficient, and accessible solution that improves decision-making and productivity in modern agriculture.

Conclusion

The Smart Krishi Assistant successfully integrates artificial intelligence and cloud technologies to provide a comprehensive agricultural support system. By combining crop recommendation, disease detection, weather forecasting, and AI assistance, the platform enhances decision-making and productivity. The system also demonstrates the practical applicability of artificial intelligence in real-world agricultural scenarios by delivering accurate predictions and timely insights. Its modular design allows easy integration of additional features and scalability for handling larger user bases. By utilizing cloud-based deployment, the system ensures availability, reliability, and efficient performance. The Smart Krishi Assistant not only improves farming practices but also contributes to the digital transformation of agriculture, making it more data-driven and technology-enabled. The system is scalable, user-friendly, and accessible, making it suitable for farmers with varying levels of technical knowledge. It bridges the gap between traditional farming and modern technology, contributing to smarter agriculture

References

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Copyright

Copyright © 2026 Chandra Sekhar N Jaladi, S V N S Pavan Kumar K, Akhila M, Chaitanya N, Roshan Kumar K. 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.