Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images

Abstract

Image-based spectral models assist in estimating the yield of maize. During the vegetative and reproductive phenological phases, the corn crop undergoes changes caused by biotic and abiotic stresses. These variations can be quantified using spectral models, which are tools that help producers to manage crops. However, defining the correct time to obtain these images remains a challenge. In this study, the possibility to estimate corn yield using multispectral images is hypothesized, while considering the optimal timing for detecting the differences caused by various phenological stages. Thus, the main objective of this work was to define the ideal phenological stage for taking multispectral images to estimate corn yield. Multispectral bands and vegetation indices derived from the Planet satellite were considered as predictor variables for the input data of the models. We used root mean square error percentage and mean absolute percentage error to evaluate the accuracy and trend of the yield estimates. The reproductive phenological phase R2 was found to be optimal for determining the spectral models based on the images, which obtained the best root mean square error percentage of 9.17% and the second-best mean absolute percentage error of 7.07%. Here, we demonstrate that it is possible to estimate yield in a corn plantation in a stage before the harvest through Planet multispectral satellite images.

Intercropped Soybean Plant Population in a Coffee Plantation and Its Effects on Agronomic Parameters and Geospatial Information

“…These images were acquired using a Mapir high-resolution camera (NDVI images) coupled with an ARP Phanton4 drone, which collects red, green, and blue (RGB) bands (green, 550 nm; red, 660 nm; and near-infrared, 850 nm), and flies 50 m above the ground, with a ground sample distance (GSD) of 1.1 cm, providing good spatial resolution images [27]. The images were captured at stage R1 (7 December 2021), because plants at this stage of development are more sensitive to spectral responses [28].…”