Watching plants grow – a position paper on computer vision and Arabidopsis thaliana

Jonathan Bell, Hannah M. Dee
2017 IET Computer Vision  
We present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can
more » ... ul measurements can be obtained by segmenting a whole plant from the background, we suggest that the increased range and precision of measurements made available by leaf-level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. We suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data-driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page. the subject of study. It is the first plant to have its genome fully sequenced [1] and being small and fast to grow to maturity (the first flowers might appear five weeks after sowing), it is easy to grow in large numbers for experimental study. One area for which it is well suited is study of the phenotype, the characteristics of an organism as influenced by its genetic constitution (its genotype) and its environment. As the phenotype is more variable than the genotype, being affected by the environment and also the passage of time, its study requires a large amount of data. There is a great deal of interest in methods that help with the (non-destructive) acquisition and the understanding of this volume of data [2] . Biologists need to measure variations in size between specimens, using such measures as fresh and dry weight and leaf area. Traditionally, taking these measurements of a plant entails harvesting the plant so an experimenter can only get one set of measurements from each specimen. If all measurements are taken at the end of an experiment then differences in rates of growth will be lost [3] . As there is a close correlation between plant weight and leaf area, [4], using images to measure leaf area gives biologists a useful measure of the size of the plant and using images as the basis of measurement means measurements can be taken non-destructively. This leads to an explosion in the amount of data that can be obtained [5] . How best to process and analyse the resulting images is itself an open and active research area and so is of interest to specialists in computer vision as well as to biologists. The factors that make it an interesting computer vision problem include:- • The shape of a plant and its leaves varies from specimen to specimen. • The shape of the plant and leaves change over time both in the short term (the possibility of leaves being blown in the wind and -over a longer period -daily changes of leaf orientation from day to night) and in the long term (the plant's growth). • As the plant grows, new leaves appear, changing the overall shape of the plant. • As leaves appear, they start to overlap and occlude each other and knowledge of the extent of this occlusion is important to the accuracy of measurements obtained. • It is a problem of tractable scale. 2
doi:10.1049/iet-cvi.2016.0127 fatcat:34hp7toydrhf7kbobwt7aq6fnq