Research Article | Open Access

Volume 2025 |Article ID 100088 | https://doi.org/10.1016/j.plaphe.2025.100088

Seeing the unseen: A novel approach to extract latent plant root traits from digital images

Mirza Shoaib ,^1,2 Adam M. Dimech,³ Simone J. Rochfort,^2,3 Christopher Topp,⁴ Matthew J. Hayden,^2,3 and Surya Kant ⁵

¹Agriculture Victoria, Grains Innovation Park, 110 Natimuk Road, Horsham, Victoria, 3400, Australia
²School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, 3083, Australia
³Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3083, Australia
⁴Donald Danforth Plant Science Center, Saint Louis, Missouri, USA
⁵Department of Ecological, Plant and Animal Science, School of Agriculture, Biomedicine & Environment, La Trobe University, Bundoora, Victoria, 3083, Australia

Abstract

A novel approach, the Algorithmic Root Trait (ART) extraction method, identifies and quantifies computationally-derived plant root traits, revealing latent patterns related to dense root clusters in digital images. Using an ensemble of multiple unsupervised machine learning algorithms and a custom algorithm, 27 ARTs were extracted reflecting dense root cluster size and spatial location. These ARTs were then used independently and in combination with Traditional Root Traits (TRTs) to classify wheat genotypes differing in drought tolerance.

ART-based models outperformed TRT-only models in drought classification (e.g., 96.3 % vs. 85.6 % accuracy). Combining ARTs and TRTs further improved accuracy to 97.4 %. Notably, 4 selected ARTs matched the performance of all 23 TRTs, offering 5.8 × higher information density (0.213 vs. 0.037 accuracy/feature). This superiority reflects the ability of ARTs to capture richer, more complex architectural information, evidenced by higher internal variability (35.59 ± 11.41 vs. 28.91 ± 14.28 for TRTs) and distinct data structures in multivariate analyses; PERMANOVA confirmed that ARTs and TRTs provide complementary insights.

Validated through experiments in controlled environments and field conditions with wheat drought-tolerant and susceptible genotypes, ART offers a scalable, customisable toolset for high-throughput phenotyping of plant roots. By bridging conventional, visually derived traits with autonomous computational analyses, this method broadens root phenotyping pipelines and underscores the value of harnessing sensor data that transcends human perception. ART thus emerges as a promising framework for revealing hidden features in plant imaging, with broader applications across plant science to deepen our understanding of crop adaptation and resilience.