Image analysis procedure for the optical scanning of fine-root dynamics: errors depending on the observer and root-viewing window size

Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.

TManual: Assistant for manually measuring length development in structures built by animals

Structures built by animals are extended phenotypes, and animal behavior can be better understood by recording the temporal development of structure construction. For most subterranean and wood-boring animals, these structures consist of gallery systems, such as burrows made by mice, tunnel foraging by termites, and nest excavation in ants. Measurement of the length development of such structures is often performed manually. However, it is time-consuming and cognitively costly to track length development in nested branching structures, hindering the quantitative determination of temporal development. Here, I introduce TManual, which aids the manual measurement of structure length development using a number of images. TManual provides a user interface to draw gallery structures and take over all other processes handling input datasets (e.g., zero-adjustment, scaling the units, measuring the length, assigning gallery identities, and extracting network structures). Thus, users can focus on the measuring process without interruptions. As examples, I provide the results of the analysis of a dataset of tunnel construction by three termite species after successfully processing 1125 images in ~3 h. The output datasets clearly visualized the interspecific variation in tunneling speed and branching structures. Furthermore, I applied TManual to a complex gallery system by another termite species and extracted network structures. Measuring the lengths of objects from images is an essential task in biological observation. TManual helps users handle many images in a realistic time scale, enabling a comparative analysis across a wide array of species. TManual does not require programming skills and outputs a tidy data frame in CSV format. Therefore, it is suitable for any user who wants to perform image analysis for length measurements.

An improved method for quantifying total fine root decomposition in plantation forests combining measurements of soil coring and minirhizotrons with a mass balance model

Accurate measurement of total fine root decomposition (the amount of dead fine roots decomposed per unit soil volume) is essential for constructing a soil carbon budget. However, the ingrowth/soil core-based models are dependent on the assumptions that fine roots in litterbags/intact cores have the same relative decomposition rate as those in intact soils and that fine root growth and death rates remain constant over time, while minirhizotrons cannot quantify the total fine root decomposition. To improve the accuracy of estimates for total fine root decomposition, we propose a new method (balanced hybrid) with two models that integrate measurements of soil coring and minirhizotrons into a mass balance model. Model input parameters were fine root biomass, necromass and turnover rate for Model 1, and fine root biomass, necromass and death rate for Model 2. We tested the balanced hybrid method in a loblolly pine plantation forest in coastal North Carolina, USA. The total decomposition rate of absorptive fine roots (ARs) (a combination of first- and second-order fine roots) using Models 1 and 2 was 107 ± 13 g m-2 year-1 and 129 ± 12 g m-2 year-1, respectively. Monthly total AR decomposition was highest from August to November, which corresponded with the highest monthly total ARs mortality. The ARs imaged by minirhizotrons well represent those growing in intact soils, evident by a significant and positive relationship between the standing biomass and the standing length. The total decomposition estimate in both models was sensitive to changes in fine root biomass, turnover rate and death rate but not to change in necromass. Compared with Model 2, Model 1 can avoid the technical difficulty of deciding dead time of individual fine roots but requires greater time and effort to accurately measure fine root biomass dynamics. The balanced hybrid method is an improved technique for measuring total fine root decomposition in plantation forests in which the estimates are based on empirical data from soil coring and minirhizotrons, moving beyond assumptions of traditional approaches.

Temperature and moisture dependence of daily growth of Scots pine (Pinus sylvestris L.) roots in Southern Finland

Scots pine (Pinus sylvestris L.) is one of the most important conifers in Northern Europe. In boreal forests, over one-third of net primary production is allocated to roots. Pioneer roots expand the horizontal and vertical root systems and transport nutrients and water from belowground to aboveground. Fibrous roots, often colonized by mycorrhiza, emerge from the pioneer roots and absorb water and nutrients from the soil. In this study, we installed three flatbed scanners to detect the daily growth of both pioneer and fibrous roots of Scots pine during the growing season of 2018, a year with an unexpected summer drought in Southern Finland. The growth rate of both types of roots had a positive relationship with temperature. However, the relations between root elongation rate and soil moisture differed significantly between scanners and between root types indicating spatial heterogeneity in soil moisture. The pioneer roots were more tolerant to severe environmental conditions than the fibrous roots. The pioneer roots initiated elongation earlier and ceased it later than the fibrous roots. Elongation ended when the temperature dropped below the threshold temperature of 4 °C for pioneer roots and 6 °C for fibrous roots. During the summer drought, the fibrous roots halted root surface area growth at the beginning of the drought, but there was no drought effect on the pioneer roots over the same period. To compare the timing of root production and the aboveground organs' production, we used the CASSIA model, which estimates the aboveground tree carbon dynamics. In this study, root growth started and ceased later than growth of aboveground organs. Pioneer roots accounted for 87% of total root productivity. We suggest that future carbon allocation models should separate the roots by root types (pioneer and fibrous), as their growth patterns are different and they have different reactions to changes in the soil environment.