Kisantra

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% --- Title --- \title{\textbf{Kisantra: A Scalable Agricultural Intelligence Platform}\[0.3ex] \large Harnessing AI and Remote Sensing for Indian Agriculture} \date{} % No date \begin{document} \maketitle

\begin{abstract} \textbf{Kisantra} is a cloud-native agricultural intelligence platform that combines satellite imagery, geospatial foundation models, and Microsoft Azure infrastructure to provide transparent, accurate, and scalable crop identification for India. It enhances subsidy distribution, insurance verification, and government planning while empowering farmers with actionable insights. \end{abstract}

\section{Introduction} Indian agriculture is characterized by fragmented landholdings, diverse cropping patterns, and frequent cloud cover. Current systems often fail to capture small or mixed farms, leading to missed subsidies and delayed insurance claims. Government agencies also operate with incomplete or delayed data, resulting in inefficient planning.

\textbf{Kisantra} was inspired by conversations with farmers and officials, aiming to build a trusted, real-time agricultural view for all stakeholders.

\section{System Overview} The platform delivers value to: \begin{itemize} \item \textbf{Farmers:} A mobile dashboard acts as digital proof of cultivation, supporting subsidies, insurance, and local market signals. \item \textbf{Government:} A geospatial monitoring system enables optimized irrigation, storage planning, and procurement. \item \textbf{Insurers:} A verification pipeline provides fast, auditable crop claims. \end{itemize}

\section{Methodology} \subsection{Data Sources} \begin{itemize} \item \textbf{SAR (Sentinel-1):} Captures canopy and structure, unaffected by clouds. \item \textbf{Optical (Sentinel-2):} Provides multispectral vegetation indicators. \end{itemize}

\subsection{AI Model Training} NASA's \textbf{Prithvi} foundation model was fine-tuned on Indian ground-truth data using Azure Machine Learning GPUs. This enabled crop-type classification at small farm scales.

\subsection{Application Stack} \begin{itemize} \item Backend: Python (FastAPI) \item Frontend: React with map visualization \item Database: Azure Cosmos DB (MongoDB API) \item Hosting: Azure App Service \end{itemize}

\section{Challenges} \begin{itemize} \item \textbf{Scale:} Processing terabytes of satellite data efficiently. \item \textbf{Compute:} Training large models on cloud GPUs. \item \textbf{UX:} Balancing farmer-friendly simplicity with analytical power. \end{itemize}

\section{Results} \begin{itemize} \item Successfully fused SAR+Optical data for robust crop identification. \item Built a prototype platform connecting farmers, insurers, and government. \item Demonstrated alignment between advanced AI and ground-level needs. \end{itemize}

\section{Lessons Learned} \begin{itemize} \item Listening to farmers is as important as coding. \item Full ML lifecycle on Azure accelerates reliable deployment. \item SAR+Optical fusion is essential for Indian conditions. \item Scaling nationally is feasible using open-source models. \end{itemize}

\section{Future Work} \begin{itemize} \item Expand coverage across states and crop types. \item Integrate weather, soil health, and water metrics. \item Add predictive features: yield forecasts, pest alerts, water-use efficiency. \end{itemize}

\section{Conclusion} Kisantra demonstrates how foundation models and multi-modal satellite data can be transformed into actionable insights for Indian agriculture. By linking farmers, insurers, and government on a single trusted platform, it strengthens financial security, improves governance, and contributes to national food resilience.

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Built With

• apis:
• azure
• azure-app-service
• azure-cosmos-db)-languages:-python-javascript-ai-&-machine-learning:-frameworks:-pytorch
• azure-data-factory
• cloud-platform-&-services:-microsoft-azure-(azure-blob-storage
• copernicus
• data
• for
• geopandas
• hugging-face-transformers
• programme
• rasterio
• satellite
• scikit-learn-foundation-model:-nasa's-prithvi-backend:-framework:-fastapi-database:-mongodb-(via-azure-cosmos-db-api)-frontend:-framework:-react-mapping-library:-leaflet.js-geospatial-technologies:-libraries:-gdal
• sentinel