DISCRIMINATION OF SUGARCANE CROP AND CANE YIELD ESTIMATION USING LANDSAT AND IRS RESOURCESAT SATELLITE DATA

8 citations as recorded by crossref.

1. Data-driven, early-season forecasts of block sugarcane yield for precision agriculture S. Han et al. https://doi.org/10.1016/j.fcr.2021.108360
2. AI-Enhanced Remote Sensing Applications in Indian Sugarcane Research: A Comprehensive Review . Vinayaka & P. Prasad https://doi.org/10.1007/s12355-024-01409-w
3. Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods A. Nihar et al. https://doi.org/10.1007/s12524-021-01444-0
4. Regional Model to Predict Sugarcane Yield Using Sentinel-2 Imagery in São Paulo State, Brazil R. Amaro et al. https://doi.org/10.1007/s12355-024-01468-z
5. Deriving Phenological Metrics from Landsat-OLI for Sugarcane Crop Type Mapping: A Case Study in North India R. Singh et al. https://doi.org/10.1007/s12524-022-01515-w
6. Satellite-Based Light Use Efficiency Model for Sugarcane Yield Estimation at Field Scale A. Nihar et al. https://doi.org/10.1007/s12524-023-01712-1
7. Integration of RGB-based vegetation index, crop surface model and object-based image analysis approach for sugarcane yield estimation using unmanned aerial vehicle S. K.C. et al. https://doi.org/10.1016/j.compag.2020.105903
8. AI-Driven Innovations in Sugarcane Farming and Sugar Industry in India: Enhancing Productivity, Efficiency, and Sustainability V. Misra et al. https://doi.org/10.1007/s12355-025-01715-x