VASUDHA: Visualise and Assess Soil Using Digital Hyperspectral Analytics

Traditional laboratory-based soil testing is time-consuming, costly, and often unable to meet the pace required under the national Soil Health Card Scheme. It also cannot support real-time decision-making for precision nutrient management. VASUDHA (Visualise and Assess Soil Using Digital Hyperspectral Analytics) was developed under ICAR-Network Program on Precision Agriculture (NePPA) to complement conventional wet laboratory analysis through a low-cost, real-time, and sustainable digital alternative.

VASUDHA is an Artificial Intelligence-based soil diagnostics platform that predicts key soil fertility parameters directly from hyperspectral signatures. The system estimates soil pH, electrical conductivity, organic carbon, available nitrogen, available phosphorus, and available potassium using VNIR/SWIR and MIR spectral domains. It supports both real-time acquisition from spectrometers and offline analysis of previously recorded spectral files, enabling flexible laboratory and field-portable workflows.

The predictive AI models were developed using one of the largest hyperspectral soil datasets in India, consisting of more than 17,000 laboratory-analysed soil samples collected across diverse agro-ecological regions. For each sample, laboratory fertility measurements were paired with reflectance and absorbance spectra, enabling the training of machine learning models that establish robust relationships between spectral signatures and soil nutrient properties. The system incorporates both generic national models and soil-type-specific models tailored to major soil classes such as alluvial, black, red, and mountain soils, improving prediction accuracy across heterogeneous landscapes.

VASUDHA operates through a multi-stage spectroscopy and machine learning pipeline. All spectra undergo standardised pre-processing including calibration, smoothing, noise reduction, normalisation, outlier handling, and resampling to model-specific wavelength grids. Feature extraction transforms processed spectra into latent variables representing information-rich spectral patterns. The software provides real-time visualisation of raw and processed spectra, tabular prediction outputs, comparative multi-sample analysis, and exportable results in CSV, PNG, and spectral formats. Predictions are categorised into agronomically relevant nutrient classes (Low, Medium, and High), enabling immediate interpretation for fertiliser recommendations without laboratory delay.

VASUDHA represents the first indigenous platform integrating hyperspectral VNIR/SWIR and MIR spectral analytics into a unified soil fertility prediction system. It enables in-situ nutrient mapping without chemical reagents, reduces labour and laboratory costs, and provides portable real-time soil diagnostics. The system is fully Python-based, customisable, and compatible with open-source scientific ecosystems. It was officially released by the Union Minister of Agriculture and Farmers Welfare and the Director General of ICAR on July 16, 2025. Its architecture supports spectroscopy-based soil testing centres capable of high-throughput processing, and can be integrated into handheld hyperspectral sensors, portable MIR devices, IoT soil monitoring systems, and precision agriculture platforms.
For additional context and detailed documentation of this use case, please refer to pages 52-53 in the attached Casebook.