Combinatorial Approaches to Image Processing and MGIDI for the Efficient Selection of Superior Rice Grain Quality Lines

Nahid Feizi, Atefeh Sabouri (), Adel Bakhshipour and Amin Abedi
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Nahid Feizi: Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Rasht 41996-13776, Iran
Atefeh Sabouri: Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Rasht 41996-13776, Iran
Adel Bakhshipour: Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht 41996-13776, Iran
Amin Abedi: Department of Plant Biotechnology, Faculty of Agricultural Sciences, University of Guilan, Rasht 41996-13776, Iran

Agriculture, 2025, vol. 15, issue 6, 1-17

Abstract: Rice is a vital staple in many countries, and as the demand for food diversity rises, the focus has shifted towards improving rice quality rather than just yield. This shift in breeders’ goals has led to the development of breeding populations aimed at comprehensively assessing rice grain appearance quality. In this regard, we developed an F 11 rice recombinant inbred line population derived from a cross between the IR28 and Shahpasand (SH) varieties and assessed the grain appearance characteristics of 151 lines and seven varieties using a computer vision system and a new generation of phenotyping tools for rapidly and accurately evaluating all grain quality-related traits. In this method, characteristics such as area, perimeter, length, width, aspect ratio, roundness, whole kernel, chalkiness, red stain, mill rate, and brown kernel were measured very quickly and precisely. To select the best lines, considering multiple traits simultaneously, we used the multi-trait genotype ideotype distance index (MGIDI) as a successful selection index. Based on the MGIDI and a 13% selection intensity, we identified 17 lines and three varieties as superior genotypes for their grain appearance quality traits. Line 59 was considered the best due to its lowest MGIDI value (0.70). Lines 19, 31, 32, 45, 50, 59, 60, 62, 73, 107, 114, 122, 125, 135, 139, 144, and 152 exhibited superior grain quality traits compared to the parents, making them high-quality candidates and indicating transgressive segregation within the current RIL population. In conclusion, the image processing technique used in this study was found to be a fast and precise tool for phenotyping in large populations, helpful in the selection process in plant breeding. Additionally, the MGIDI, by considering multiple traits simultaneously, can help breeders select high-quality genotypes that better match consumer preferences.

Keywords: factor analysis; machine vision; selection differential; selection gains (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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