Demeter - The Farming Assistant

Abstract

India\'s agri-sector accounts for 18% of GDP but suffers from systemic inefficiencies such as delayed disease detection (accounting for 30% annual losses), fertilizer overuse, and inaccessibility of experts. Demeter is a system combining computer vision (CNN), ensemble learning (Random Forest), and NLP to offer: (1) Real-time disease identification through leaf image analysis (98.7% accuracy), (2) Data-based crop/fertilizer suggestions (97.3% accuracy), and (3) Multilingual chatbot support. The following paper elaborates on system architecture, ML model training, and field trial outcomes from over 250 farmers in Maharashtra.

Introduction

Agriculture remains a cornerstone of India’s economy, supporting 58% of the population and contributing 18% to GDP. However, smallholder farmers face challenges such as late disease detection causing up to 40% crop loss, inefficient fertilizer use leading to environmental damage, and limited access to specialized knowledge due to language and infrastructure barriers. Existing digital tools are fragmented and do not provide a unified solution.

Demeter – The Farming Assistant is an AI-powered, integrated platform designed to revolutionize Indian agriculture by combining computer vision, predictive analytics, and natural language processing in one accessible system. It uses convolutional neural networks (CNNs) to detect crop diseases from leaf images with 98.7% accuracy in real time, far faster than manual methods. Machine learning models analyze soil nutrition, climate, and yield data to offer highly accurate, personalized crop and fertilizer recommendations. The platform supports offline usage via a Progressive Web App, offers multilingual chatbot support in eight regional languages, and secures data with blockchain technology.

Field trials in Maharashtra demonstrated a 22% average increase in crop yields and 35% reduction in fertilizer costs, highlighting the platform’s potential to improve both economic and environmental outcomes.

Additional Context:

• The literature review covers AI/ML applications in crop forecasting, disease detection, fertilizer recommendations, and multilingual chatbots, emphasizing the potential and challenges of integrating these technologies in rural India.

• Traditional farming is cost-effective and eco-friendly but lacks productivity and data-driven insights. Modern farming boosts yields and uses data analytics but faces issues like high costs and tech literacy demands.

• Demeter integrates diverse technologies (Next.js, Tailwind CSS, Flask, TensorFlow, APIs) to deliver a seamless, localized farming assistant experience accessible even in areas with poor connectivity.

Conclusion

Demeter – The Farming Assistant solves the big challenges for Indian farmers by combining AI, machine learning, and web technologies into one platform that offers clever agricultural assistance. Conventional farming practices tend to use guesswork and do not have access to real-time data, leading to mediocre yields and resource wastage. Demeter counters these issues through features like crop disease detection using CNN models, AI-powered crop and fertilizer recommendations, real-time weather forecasting, and multilingual chatbot assistance. It leverages large-scale datasets, OpenAI’s language models, and external APIs like OpenWeather and Google Maps to deliver precise, localized insights to farmers. With a user-friendly Next.js interface, Flask-based backend, and secure API integrations, Demeter ensures accessibility and scalability across rural areas. Although obstacles such as digital literacy and connectivity remain, prospective developments like voice interfaces and IoT are poised to enlarge its reach. In sum, Demeter shows that AI-powered, farmer-focused platforms can revolutionize farming by encouraging sustainability, improving productivity, and equipping farmers with data-informed choices for a more robust future for agriculture..

References

[1] Elbasi, E., Zaki, C., Topcu, A.E., Abdelbaki, W., Zreikat, A.I., Cina, E., Shdefat, A., & Saker, L. (2023). Crop Prediction Model Using Machine Learning Algorithms.Applied Sciences, 13(16), 9288 [2] Kulkarni, P., Karwande, A., Kolhe, T., Kamble, S., Joshi, A., & Wyawahare, M. (2021). Plant Disease Detection Using Image Processing and Machine Learning [3] Anitha, M., Reddy, CH. S., & Deepika, CH. (2023). Agriculture Helper Chatbot Using Deep Learning.International Research Journal of Modernization in Engineering Technology and Science, 5(7). [4] Sharma, R., Singh, A., Rampal, Chaurasiya, R.K., & Kumar, A. (2023). Fertilizer Recommendation and Crop Prediction using Machine Learning Techniques.International Journal of Research Publication and Reviews

Copyright

Copyright © 2025 Archit Pathrikar, Harshvardhan Patilkhede, Rahul Rade, Divyansh Singh, Prof. Priyanka Karhade. 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.