Abdul Khalid – Assistant Professor (IT), NIET, Greater Noida, India. abdulkhalid@niet.co.in
Abstract
This study presents an **AI-powered agricultural assistance system** integrating **text-based consultancy, voice interaction, and image-based disease detection** using **deep learning and Retrieval-Augmented Generation (RAG)**. The system employs the **Llama 3.2 3B model**, ensuring **up-to-date agricultural knowledge**, while locally processing queries and images via a CNN model trained on **61,486 plant disease images from 39 categories**.
The **Smart Agriculture Assistant** integrates **AI-powered consulting and disease detection** to assist farmers with **real-time, customized crop health insights**. Future developments will focus on **multilingual support, offline functionality, and IoT integration for environmental monitoring**, expanding the **scalability and accessibility of AI-driven farming solutions**.
References
D. Kavitha et al. (2024). “A Smart Agricultural Assistant for Crop Recommendation using Machine Learning.” Grenze International Journal of Engineering and Technology.
Ayaz et al. (2024). “Revolutionizing Agriculture with Artificial Intelligence: Plant Disease Detection.” Frontiers in Plant Science.
N. N. H. Nik Hashim et al. (2024). “Artificial Intelligence and IoT-Based Intelligent Farming System for Dragon Fruit Using YOLOv8 Algorithm.” Lecture Notes in Electrical Engineering.
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