Abstract
Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future.
Highlights
Rice, a staple food that feeds half of the world’s population (Yang et al, 2013), is one of the most water-hungry crops (Xia et al, 2006)
This paper demonstrates the use of high-throughput phenotyping and image analysis to dynamically quantify rice drought response in vivo in a non-intrusive manner
total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR), and total plant area/convex hull area ratio (TCR), were derived from the images and their behavior is related to changes in the leaf
Summary
A staple food that feeds half of the world’s population (Yang et al, 2013), is one of the most water-hungry crops (Xia et al, 2006). Widespread pollution, climatic change, and a growing population further aggravate the water shortage problem. When drought stress occurs during the reproductive stage, rice yield is very significantly reduced (Venuprasad et al, 2009; Vikram et al, 2011). Drought resistant varieties assist in maintaining and improving rice yield under such conditions. As in many crops, one of the major targets in breeding is to develop drought resistant varieties. To accelerate the identification of novel drought resistant varieties in breeding programs, a rapid species-agnostic method for evaluation of drought-resistance under diverse conditions is a necessary prerequisite. Since cost-effective solutions are likely to be based on visible attributes, we explored the readily observable (macroscopic) reactions of leaves to drought
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