Year: 2018
Type: review
Abstract: Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e. its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting 'big-data' is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts.
Source: Current Opinion in Biotechnology
Institution: University of Nottingham
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $4,740
Awards: Hydropatterning, G1301833649, LeMuR: Plant Root Phenotyping via Learned Multi-resolution Image Segmentation, FUTUREROOTS: Redesigning root architecture for improved crop performance, Bridging the Genotype to Phenotype Gap: Uncovering root anatomical, architectural and field traits., G962009147
OpenAlex ID: W2883418202