In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning–based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.

Photosynthetic acclimation (photoacclimation) is the process whereby leaves alter their morphology and/or biochemistry to optimize photosynthetic efficiency and productivity according to long-term changes in the light environment. The three-dimensional architecture of plant canopies imposes complex light dynamics, but the drivers for photoacclimation in such fluctuating environments are poorly understood. A technique for high-resolution three-dimensional reconstruction was combined with ray tracing to simulate a daily time course of radiation profiles for architecturally contrasting field-grown wheat () canopies. An empirical model of photoacclimation was adapted to predict the optimal distribution of photosynthesis according to the fluctuating light patterns throughout the canopies. While the photoacclimation model output showed good correlation with field-measured gas-exchange data at the top of the canopy, it predicted a lower optimal light-saturated rate of photosynthesis at the base. Leaf Rubisco and protein contents were consistent with the measured optimal light-saturated rate of photosynthesis. We conclude that, although the photosynthetic capacity of leaves is high enough to exploit brief periods of high light within the canopy (particularly toward the base), the frequency and duration of such sunflecks are too small to make acclimation a viable strategy in terms of carbon gain. This suboptimal acclimation renders a large portion of residual photosynthetic capacity unused and reduces photosynthetic nitrogen use efficiency at the canopy level, with further implications for photosynthetic productivity. It is argued that (1) this represents an untapped source of photosynthetic potential and (2) canopy nitrogen could be lowered with no detriment to carbon gain or grain protein content.

Analysis of synthesis mutants demonstrates an ascorbate requirement for growth under low light and for high light-dependent anthocyanin accumulation, but no consistent effects on photoinhibition or zeaxanthin accumulation were found.

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Photoinhibition is the light-induced reduction in photosynthetic efficiency and is usually associated with damage to the D1 photosystem II (PSII) reaction centre protein. This damage must either be repaired, through the PSII repair cycle, or prevented in the first place by nonphotochemical quenching (NPQ). Both NPQ and D1 repair contribute to light tolerance because they ensure the long-term maintenance of the highest quantum yield of PSII. However, the relative contribution of each of these processes is yet to be elucidated. The application of a pulse amplitude modulation fluorescence methodology, called protective NPQ, enabled us to evaluate of the protective effectiveness of the processes. Within this study, the contribution of NPQ and D1 repair to the photoprotective capacity of Arabidopsis thaliana was elucidated by using inhibitors and mutants known to affect each process. We conclude that NPQ contributes a greater amount to the maintenance of a high PSII yield than D1 repair under short periods of illumination. This research further supports the role of protective components of NPQ during light fluctuations and the value of protective NPQ and q as unambiguous fluorescence parameters, as opposed to q and F /F , for quantifying photoinactivation of reaction centre II and light tolerance of photosynthetic organisms.