Assessing Drought Stress in Legumes using Block-Chain-Assisted Drone-based Spectral Sensors

Drought stress significantly limits the growth and productivity of legume crops, posing a serious threat to global food security. Conventional drought detection methods are often labor-intensive, subjective and inadequate for large-scale monitoring. While drone-based spectral sensing offers precise and high-resolution assessment of crop stress, challenges remain in data security, transparency and traceability. Integrating blockchain technology with remote sensing provides a robust framework for secure, tamper-proof storage and real-time sharing of drought assessment data.

Legume crops were subjected to controlled irrigation treatments, including moderate and severe drought stress conditions. Drone-mounted spectral sensors captured high-resolution data to compute vegetation indices reflecting physiological indicators such as leaf water content, chlorophyll activity and canopy temperature. These datasets were securely recorded on a blockchain platform to ensure data integrity and accountability. Machine learning algorithms, particularly the Random Forest model, were employed to classify drought stress levels, while smart contracts enabled automated validation and traceable data management.

Drought stress resulted in significant reductions in vegetation index values and increased canopy temperatures, confirming deterioration in plant physiological health. The blockchain-assisted system ensured secure, transparent and immutable storage of spectral data, enhancing trust in the monitoring process. The Random Forest model demonstrated high classification accuracy in distinguishing stress levels, while spatial stress maps enabled precise identification of affected zones, supporting optimized irrigation and site-specific management in precision agriculture.