Irrigated corn grain yield prediction in Florida using active sensors and plant height

Abstract

Remote sensing is widely utilized in agriculture for estimating corn (Zea mays L.) grain yield (CGY). Few studies have determined if the Normalized Difference Vegetation Index (NDVI) and/or Soil Plant Analysis Development (SPAD) can estimate CGY in Florida. From April to August 2022, in Live Oak, Florida, a field-scale experiment was conducted in two sites with irrigated corn using a complete randomized block design with six nitrogen (N) rates and four replicates per site. This study aimed to estimate CGY using NDVI alone or in combination with SPAD, plant height (PH), and N rate. CGY response curve served as a comparison standard. Fifteen data subsets were selected, and stepwise selection multiple linear regression analysis was utilized to generate each reduced equation (Model). In addition, the relative significance of the predictor variables was evaluated. The strongest correlations with CGY were demonstrated by N rate (r = 0.93), PH103 (r = 0.91), NDVI39 (r = 0.81), and SPAD60 (r = 0.93). Models with multiple variables showed a better fit than single-variable models. Model 15 (variables until tasseling - 60 DAP) demonstrated comparable performance with 92.8% of variance explained and RMSE = 1,315.685 kg ha−1. Regardless of the model, the N rate has always contributed the most to CGY. Although Model 1 had the best overall performance, it may not be feasible for growers to utilize a model with multiple terms. Consequently, Model 15 could estimate CGY in Florida based on PH and NDVI at 60 and 32 DAP, respectively.

Keywords

• Multiple regression
• NDVI
• Nitrogen
• Remote sensing
• SPAD
• Zea mays