Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R² ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R² = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Terminal drought causes major yield loss in chickpea, so it is imperative to identify genotypes with best suited adaptive traits to secure yield in terminal drought-prone environments. Here, we evaluated chickpea (At) rd29A:: (At) DREB1A transgenic events (RD2, RD7, RD9 and RD10) and their untransformed C235 genotype for growth, water use and yield under terminal water-stress (WS) and well-watered (WW) conditions. The assessment was made across three lysimetric trials conducted in contained environments in the greenhouse (2009GH and 2010GH) and the field (2010F). Results from the greenhouse trials showed genotypic variation for harvest index (HI), yield, temporal pattern of flowering and seed filling, temporal pattern of water uptake across crop cycle, and transpiration efficiency (TE) under terminal WS conditions. The mechanisms underlying the yield gain in the WS transgenic events under 2009GH trial was related to conserving water for the reproductive stage in RD7, and setting seeds early in RD10. Water conservation also led to a lower percentage of flower and pod abortion in both RD7 and RD10. Similarly, in the 2010GH trial, reduced water extraction during vegetative stage in events RD2, RD7 and RD9 was critical for better seed filling in the pods produced from late flowers in RD2, and reduced percentage of flower and pod abortion in RD2 and RD9. However, in the 2010F trial, the increased seed yield and HI in RD9 compared with C235 came along only with small changes in water uptake and podding pattern, probably not causal. Events RD2 (2010GH), RD7 (2010GH) and RD10 (2009GH) with higher seed yield also had higher TE than C235. The results suggest that DREB1A, a transcription factor involved in the regulation of several genes of abiotic stress response cascade, influenced the pattern of water uptake and flowering across the crop cycle, leading to reduction in the percentage of flower and pod abortion in the glasshouse trials.

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.