BackgroundWheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations.ResultsIn this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress.ConclusionsBeing able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-017-0229-8) contains supplementary material, which is available to authorized users.

The robots that operate autonomously for extended periods in remote environments are often limited to gather only small amounts of power through photovoltaic solar panels. Such limited power budgets make power management critical to the success of the robot's mission. Artificial endocrine controllers, inspired by the mammalian endocrine system, have shown potential as a method for managing competing demands, gradually switching between behaviors, synchronizing behavior with external events, and maintaining a stable internal state of the robot. This paper reports the results obtained using these methods to manage power in an autonomous sailing robot. Artificial neural networks are used for sail and rudder control, while an artificial endocrine controller modulates the magnitude of actuator movements in response to battery or sunlight levels. Experiments are performed both in simulation and using a real robot. In simulation a 13-fold reduction in median power consumption is achieved; in the robot this is reduced to a twofold reduction because of the limitations of the simulation model. Additional simulations of a long term mission demonstrate the controller's ability to make gradual behavioral transitions and to synchronize behaviors with diurnal and seasonal changes in sunlight levels.

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