Data for proximal sensing of maize nutrient status

Le résumé fourni par la source

Optimization of fertilization is important for enhancing the economic and environmental sustainability of corn production. However, evaluating nutrient status by manual sampling and analysis is time-consuming, laborious and expensive. The objective of this research was to predict nutrient status of field-grown corn based on hyperspectral data collected by proximal sensing of corn ear leaves. Transmittance in a range from 459 to 943 nm was acquired by a handheld device followed by measurement of N, K, Mg, Ca, P, S, Fe, Al, Mn, Zn, Cu and B on the same leaves. Models were developed to predict nutrient concentrations based on spectral data using support vector regression (SVR). Coefficients of determination (R2) of leave-one-out cross-validation (CV) were calculated to evaluate the performance of the models. Models for proximal sensing of N, K, Mg, Ca, P, S, Mn, Zn and B performed reasonably well for studies conducted in 2020 and 2021. We concluded that proximal hyperspectral sensing can provide acceptable predictions on all macronutrients (N, K, Mg, Ca, P, S) and some micronutrients (Mn, Zn, B). These low-cost and high-throughput predictions of plant nutrient status will contribute to more sustainable corn production.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.