Machine learning for measuring roots

3rd April 2019

Researchers from the Centre for Research in Agricultural Genomics (CRAG) and La Salle-Ramon Llull University, both in Barcelona, Spain, have developed a software that, through image processing and machine learning, allows researchers to semi-automate the analysis of root growth of Arabidopsis thaliana seedlings growing directly in agar plates.

The software, named MyRoot, has been made available to the research community free of charge. CRAG researchers have already saved significant labor and time using MyRoot. The high efficiency and accuracy offered by MyRoot has been demonstrated in an article that is recently published in The Plant Journal.

The root: a key element for the agriculture

The root, which is responsible for anchoring the plant to the soil, is an essential organ for overall plant growth and development. Roots provide the necessary structural and functional support for the incorporation of nutrients and water from the soil. Characterization of different root traits is therefore important not only for understanding organ growth, but also for evaluating the impact of roots in agriculture. At CRAG, the research group led by Ana I. Caño-Delgado studies steroid hormone signaling effects on root development, using the small model plant Arabidopsis thaliana. To do so, researchers at Caño-Delgado's laboratory must measure the root length of a large number of arabidopsis seedlings holding different genetic modifications and exposed to different conditions. Thanks to these investigations, they recently discovered how to create drought resistant plants, without affecting their growth.

Isabel Betegón-Putze has spent three years doing her doctoral thesis at CRAG, and during this time she has spent many hours measuring arabidopsis roots with the photos she takes. "I had tried some semi-automatic analysis softwares but they were not accurate enough and were very difficult to use," explains Betegón-Putze. Her thesis director, Ana I. Caño-Delgado, proposed to collaborate with the engineer Xavier Sevillano, from the Research Group in Media Technologies at La Salle, to develop a new software that streamlined this process.

The full article can be read here