ASSESSMENT OF RICE BIOMASS PRODUCTION AND YIELD USING SEMI-PHYSICAL APPROACH AND REMOTELY SENSED DATA

7 citations as recorded by crossref.

1. Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges J. Wani et al. 10.1007/s11831-021-09588-5
2. Satellite-Based Light Use Efficiency Model for Sugarcane Yield Estimation at Field Scale A. Nihar et al. 10.1007/s12524-023-01712-1
3. Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model S. Pazhanivelan et al. 10.3390/agronomy12092008
4. Comparison of Three Different Satellite-Based Approaches for Aboveground Biomass Estimation A. Mokhtari et al. 10.1007/s41064-020-00134-9
5. Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms M. Gumma et al. 10.3390/agriengineering6010045
6. Spatial Variability Assessment on Staple Crop Yields in Hisar District of Haryana, India Using GIS and Remote Sensing S. Banerjee et al. 10.32604/rig.2025.057963
7. Towards Fine-Scale Yield Prediction of Three Major Crops of India Using Data from Multiple Satellite R. Tripathy et al. 10.1007/s12524-021-01361-2