Background and objectives: Lentil (Lens Culinaris. Medik) is a highly nutritious food staple widely consumed within India subcontinent and the Mediterranean region. Although gaining popularity in western diets, wheat will continue to be a major crop as it can be used to manufacture a wide range of products. The nutritional benefits of lentils are acknowledged, particularly as a source of high protein so the incorporation of lentil flour into wheat-based foods has the potential to

Atmospheric CO2 concentrations have been increasing from ∼280 to 405 mmol mol−1 air from the preindustrial era until now. As this rise is a major driver for global warming and increasing variability in weather patterns, it is predicted that the frequency and duration of heat waves will continue to increase in many arable regions during this century. Lentil (Lens culinaris Medik.) is a cool‐season crop whose production has recently expanded into areas where it is subject to high temperature stress during pod filling (e.g., Australia). The objective of this experiment was to determine whether growth at elevated atmospheric CO2 concentrations (e[CO2], imposed by free‐air CO2 enrichment [FACE]) is able to compensate for the negative impact of a 3‐d heat wave event imposed at the flat pod stage on two lines of lentil. Grain yield under e[CO2] subjected to the heat wave were equivalent to grain yield under ambient without the heat wave event. The heat wave reduced grain yield by 33%, but this was not made more or less severe by e[CO2]. This reduction was attributed to a small decrease in aboveground biomass (6%) and a larger decrease in harvest index (16%) due to the heat wave event. The number of pods and grains per square meter were reduced by the heat wave (29–32%), whereas seed size was not affected. The effects of the heat wave during the event were evident on the foliar canopy temperature measured with an infrared thermometer, which increased by 6°C, and on the electron transport rate calculated from the quantum efficiency of photosystem II obtained with chlorophyll fluorescence measurements.

A global predictive model was developed for protein, moisture, and grain type, using near infrared (NIR) spectra. The model is a deep convolutional neural network, trained on NIR spectral data captured from wheat, barley, field pea, and lentil whole grains. The deep learning model performs multi-task learning to simultaneously predict grain protein, moisture, and type, with a significant reduction in prediction errors compared to linear approaches (e.g., partial least squares regression). Moreover, it is shown that the convolutional network architecture learns much more efficiently than simple feedforward neural network architectures of the same size. Thus, in addition to improved accuracy, the presented deep network is very efficient to implement, both in terms of model development time, and the required computational resources.

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