Application of support vector machine method in simulating barley wield using drought indices

Timely and accurate crop yield estimation is important to regulate crop management and ensure agricultural sustainability. This research investigates the application of the support vector regression method in simulating the yield of barley in ten selected farms in the Qazvin plain from 2019 to 2020 using the drought indices NDVI, MSAVI, and EVI. Sentinel 2 satellite was used for drought indices. The results were evaluated using R2, RMSE, and ME statistics. To investigate the application of the support vector regression method in simulating the yield of barley using the mentioned drought indicators, seven scenarios were defined. The results of evaluating the relationship between NDVI, MSAVI, and EVI indices with yield barley showed that the support vector regression method in all scenarios except the first scenario in the test phase with a significant probability of 95% (P-value=0.0) and an explanatory coefficient of more than 0.7 and the low value of the RMSE index has a good estimate of the yield of the barley product. The results of this research will be useful in managing and planning the development of barley cultivation based on drought indices in the future.