Prediction of dry direct-seeded rice yields using chlorophyll meter, leaf color chart and GreenSeeker optical sensor in northwestern India

摘要: Abstract Prediction of potential yields during crop growth season is important for successful agricultural decision-making. The objective this study was to predict grain yield dry direct-seeded rice (DDSR) using leaf greenness as measured by chlorophyll meter (SPAD) and color chart (LCC) normalized difference vegetation index (NDVI) worked from GreenSeeker optical sensor measurements. Regression analysis performed at maturity the LCC, SPAD NDVI readings recorded two multi-rate nitrogen level experiments conducted in consecutive seasons. measurements made early stage could not explain satisfactorily variations yield. Predictions LCC were reliable. superior booting stage. panicle initiation exhibited highest coefficient determination explained 63% variation Yield predictability with 70 84 DAS, 70, 98 DAS did improve introducing element cumulative growing degree days (CGDD). However, CGDD based improved predictability. regression models validated on an independent data set obtained experiment same area. root mean square error (RMSE) lower than readings. On contrary, adjusting score reduced RMSE. reveals that DDSR can be predicted in-season NDVI, scores adjusted CGDD.