An efficient subsampling method for estimating corn root characteristics with scanner-based image analysis

Authors: AMPONG, KWAME - Purdue University; Penn, Chad; CAMBERATO, JAMES - Purdue University; Williams, Mark

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/21/2024
Publication Date: 7/20/2024
Citation: Ampong, K., Penn, C.J., Camberato, J., Williams, M.R. 2024. An efficient subsampling method for estimating corn root characteristics with scanner-based image analysis. Agronomy Journal. https://doi.org/10.1002/agj2.21645.
DOI: https://doi.org/10.1002/agj2.21645

Interpretive Summary: Estimating corn root dimensions is important to develop crop and nutrient uptake models. Such models can help farmers to predict water and fertilizer use as well as grain yields. However, measuring total root length, root diameter, and root surface area are difficult and require excessive time and money. The research goal is to develop a new sampling method for estimating corn roots that is time-consuming and cost-efficient. Complete corn root balls from plants were collected from an indoor grow room using two different sample methods: a visual and a mass-based sample method. Before gathering the samples, the corn root samples were separated into coarse root and fine roots. Each of the two sample groups with 65 samples each were then separated visually and scanned as an image on a root scanner. Then the same roots were subsampled by mass, to achieve 1/65 of the total mass, prior to scanning. Statistics showed variability in total root length, root diameter, and root surface area; this data was used to determine the minimum number of subsamples for estimating the entire root system. The results showed that the mass-base method needed fifteen samples on a scanner in comparison to sixty samples by the visual-based separation technique. Therefore, this new technique will save working hours and reduce the cost of assessing root system characteristics.

Technical Abstract: Quantifying root length, surface area, average diameter, and volume of fully matured corn (Zea mays L.) is labor intensive, time consuming, and costly. Accurate and efficient subsampling techniques are needed to overcome these limitations. In this study, 8 corn root systems were grown to full maturity using a sand-culture hydroponics system. The entire root system was initially separated into coarse and fine roots. Coarse and fine roots were then composited into 65 subsamples either visually or by mass, followed by subsample scanning to quantify root characteristics. A bootstrap non-parametric procedure was used to determine the sample size needed to represent the total root system and quantify the uncertainty based on the number of subsamples analyzed. When subsamples were composited visually, as many as 60 subsamples (92% of the total root system) were necessary to represent the characteristics of the root system within +/- 5% of the true mean at a 95% confidence level. In contrast, when subsamples were composited by mass, a maximum of 15 subsamples (23% of the total root system) were needed to be representative. The findings show that separating the entire root system by coarse and fine roots and then weighing it into equal subsamples before scanning decreased the number of subsamples required to accurately estimate corn root characteristics. Thus, this approach considerably reduces the required time, effort, and cost of processing corn root systems.