Next-generation tools for nutrition-inclusive breeding for cereals
2025
Choudhary, S. | Anbazhagan, Krithika | Kholova, J. | Murugesan, T. | Kaliamoorthy, S. | Chadalawada, K. | Prasad, Kodukula V.S.V. | Nankar, A.N. | Mani, V. | Chandra, M. | Banoriya, R. | Vadez, V.
Addressing global malnutrition requires improving the nutritional quality of major crops and promoting nutritionally rich crops. However, breeding for improving nutritional traits is challenging, particularly in the absence of rapid and precise phenotyping of these parameters. Quick phenotyping is crucial as it allows breeders to select lines with high nutritional value alongside yield and other important traits while advancing the generations. Traditionally, grain nutritional and quality assessments have relied on wet-lab analytical services, which are slow, costly, and often inaccessible. To overcome these limitations, rapid and cost-effective sensor-based technologies have emerged as a promising solution. Interdisciplinary research combining sensor technology, AI, biochemistry, and crop science has significantly advancing the grain composition analysis, and post-harvest trait evaluation. Tools like near-infrared spectroscopy (NIRS), X-ray fluorescence (XRF), and computer tomography (CT) are increasingly getting utilized to ensure quality standards in trade, nutrition, and food safety. These technologies focus on key traits precisely, time, and cost-effectively, with early findings highlighting their potential for scalable solutions. Such advancements are essential for nutrition-sensitive breeding and improving food safety, quality-based payments for farmers, and supporting global efforts against malnutrition. The swift adoption of these technologies in breeding programs, supported by public-private partnerships, is crucial for sustainable development.
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