Key points are not available for this paper at this time.
Abstract A precise estimation of the yield is crucial to maintain well-functioning food security at every tier. It also ensures the income of the producers dependent on the recurrent economic practice of crop insurance. The yield estimation methods used earlier were cost-ineffective and ominous. To overcome it, a semi-physical approach was applied with the help of satellite data to estimate the yield of major crops grown in the Sabarkantha district of Gujarat. Data from Sentinel 2B has been stacked throughout the Rabi season at equal intervals and classified using supervised training sets for the study area. MOD11A2 Version 6, an eight-day composite image of Rabi season, was compiled to analyze the mean night and daytime land surface temperature that depicts the status of temperature stress. The net Primary Product (NPP) of the crops was evaluated with the assistance of a semi-physical model. The accuracy of the result was assessed with the help of the ground truth points. Grain yield results were compared with the average yield statistics that showed Root Mean Square Error (RMSE) deviation of 0.38 for wheat, 0.15 for maize, and 0.19 for both gram and mustard. In conclusion, the semi-physical approach of crop yield estimation meets the accuracy requirements, is feasible, and can be used for various crops. In addition, it is convenient and inexpensive.
Roy et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: