Using Digital Image Analysis to Estimate Corn Ear Traits in Agrotechnical Field Trials: The Case with Harvest Residues and Fertilization Regimes

Abstract

In this study, we aimed to evaluate the feasibility of digital image analysis (DIA) as a substitute for standard analysis (SA) in assessing corn ear traits in agrotechnical field trials. Accurate and timely prediction of corn yield through corn ear traits can lead to precise agricultural management recommendations for the improvement of production. Four replications with 10 plots each were subjected to different fertilization regimes and analyzed using DIA and SA to determine the kernel number per ear (KN), ear length (EL), and ear diameter (ED). For both methods, the results showed that only nitrogen doses had a significant effect on the examined corn ear traits, and the correlation matrix revealed a strong and significant relationship between yield and corn ear traits. The post-hoc test showed no discrepancy in cases between the two methods for KN and EL, with a 6.7% discrepancy for ED. For both methods, a linear plateau was the best fit for KN and EL with increasing nitrogen doses, whereas a quadratic plateau was the best fit for ED. The regression equations for both methods provided similar recommendations regarding nitrogen requirements. The findings suggest that DIA can be used as a substitute for SA of corn ear traits obtained from different fertilization variants and can provide nitrogen fertilization recommendations for optimal corn yields.