Harvest Timing of Standing Corn Using Near-Infrared Reflectance Spectroscopy

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Spectroscopy

Background:

• Accurate corn maturity assessment is crucial for optimizing nutritive value in livestock feed.
• Whole-plant moisture content is a common but labor-intensive indicator of corn maturity.
• Current methods for measuring standing corn moisture are time-consuming and require specialized farm equipment.

Purpose of the Study:

• To evaluate efficient and accurate methods for estimating standing whole-plant corn moisture content.
• To develop a predictive model for whole-plant moisture based on ear moisture measurements.
• To assess the feasibility of using handheld near-infrared reflectance (NIR) spectrometers for this application.

Main Methods:

• Developed a partial least squares regression model using reference data from 610 corn plants in the 2021 growing season.
• Predicted ear moisture using handheld NIR spectrometers and applied a relationship to estimate whole-plant moisture.
• Validated the model with 330 corn plants from the 2022 growing season.
• Evaluated the transferability of calibrations across three NIR spectrometers.

Main Results:

• The ear moisture model accurately predicted whole-plant moisture with a standard error of prediction of 2.7 and R²P of 0.88 in the validation dataset.
• Ear moisture content ranged from 26 to 80 %w.b., corresponding to whole-plant moisture from 55 to 81 %w.b.
• Significant spectrometer-to-spectrometer differences were observed, which can be reduced by including multiple spectrometers in calibration.

Conclusions:

• Handheld NIR spectroscopy offers a promising, convenient, and accurate method for estimating corn maturity via ear moisture.
• The developed model effectively predicts whole-plant moisture, simplifying a critical aspect of livestock feed management.
• Further research is needed to ensure calibration stability across diverse geographical regions and long-term use.