L-system models for image-based phenomics: case studies of maize and canola
Mikolaj Cieslak, Nazifa Azam Khan, Pascal Ferraro, Raju Soolanayakanahally, Stephen J. Robinson, Isobel A. P. Parkin, Ian McQuillan, Przemysław Prusinkiewicz
Abstract
Abstract Artificial neural networks that recognize and quantify relevant aspects of crop plants show great promise in image-based phenomics, but their training requires many annotated images. The acquisition of these images is comparatively simple, but their manual annotation is time-consuming. Realistic plant models, which can be annotated automatically, thus present an attractive alternative to real plant images for training purposes. Here we show how such models can be constructed and calibrated quickly, using maize and canola as case studies.
Topics & Concepts
PhenomicsCanolaArtificial intelligenceComputer scienceAnnotationMachine learningPattern recognition (psychology)Agricultural engineeringAgronomyEngineeringBiologyGenomicsBiochemistryGenomeGeneGreenhouse Technology and Climate ControlSmart Agriculture and AIRemote Sensing in Agriculture