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

8 citations as recorded by crossref.

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2. AI-Enhanced Remote Sensing Applications in Indian Sugarcane Research: A Comprehensive Review . Vinayaka & P. Prasad
3. Sugarcane Crop Type Discrimination and Area Mapping at Field Scale Using Sentinel Images and Machine Learning Methods A. Nihar et al.
4. Regional Model to Predict Sugarcane Yield Using Sentinel-2 Imagery in São Paulo State, Brazil R. Amaro et al.
5. Deriving Phenological Metrics from Landsat-OLI for Sugarcane Crop Type Mapping: A Case Study in North India R. Singh et al.
6. Satellite-Based Light Use Efficiency Model for Sugarcane Yield Estimation at Field Scale A. Nihar et al.
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.
8. AI-Driven Innovations in Sugarcane Farming and Sugar Industry in India: Enhancing Productivity, Efficiency, and Sustainability V. Misra et al.