Abstract
Almost all the world's food is grown in open fields, where plant phenotypes can be very different from those observed in greenhouses. Geneticists and agronomists studying food crops routinely detect, measure, and classify a wide variety of phenotypes in fields that contain many visually distinct types of a single crop. Augmenting humans in these tasks by automatically interpreting images raises some important and nontrivial challenges for research in computer vision. Nonetheless, the rewards for overcoming these obstacles could be exceptionally high for today's 7 billion people, let alone the 9.6 billion projected by 2050 (United Nations Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2012 Revision). To stimulate dialog between researchers in computer vision and those in genetics and agronomy, we offer our views on three computational challenges that are central to many phenotyping tasks. These are disambiguating one plant from another; assigning an individual plant's organs to it; and identifying field phenotypes from those shown in archival images. We illustrate these challenges with annotated photographs of maize highlighting the regions of interest. We also describe some of the experimental, logistical, and photographic constraints on image collection and processing. While collecting the data sets needed for algorithmic experiments requires sustained collaboration and funding, the images we show and have posted should allow one to consider the problems, think of possible approaches, and decide on the next steps.
Highlights
Increasing food security and for the future relies heavily on identifying and understanding beneficial phenotypes in crop plants
Because phenotyping involves the analysis of multiple individual plants, the machine vision challenges posed by crop fields used in genetic and agronomic research are quite different from those presented by fields in production agriculture or yield trials
A field will be planted with a single variety that will exhibit much less phenotypic variation than in the genetically diverse populations of research fields
Summary
Increasing food security and for the future relies heavily on identifying and understanding beneficial phenotypes in crop plants. Because phenotyping involves the analysis of multiple individual plants, the machine vision challenges posed by crop fields used in genetic and agronomic research are quite different from those presented by fields in production agriculture or yield trials. In the latter, a field will be planted with a single variety that will exhibit much less phenotypic variation than in the genetically diverse populations of research fields. Over a hundred years of intensive study of this important cereal crop, mostly in farm fields, has identified many genetically different varieties of maize Their phenotypes vary widely in size; shape; Fig. 2 Disambiguation of a typical maize plant at flowering. Starting algorithm development with maize is advantageous because the plants are larger, less densely planted, and more distinct from many weeds than rice or wheat
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