High-Throughput Digital Imaging Analysis Of Historical Wheat Cultivars From Pakistan

Author:

Tahir Tehreem1,Rasheed Awais2,Kayani Sadaf1,Shahzad Asim1ORCID

Affiliation:

1. Mohi-ud-Din Islamic University

2. Quaid-i-Azam University Islamabad: Quaid-i-Azam University

Abstract

Abstract Wheat is the most significant cultivated crop in Pakistan, ranking first in terms of acreage and production among all cereals. Wheat grain morphology has been harvested and manipulated since the dawn of agriculture, and it remains a major breeding focus today. To establish, genetic basis of phenotypic variation in wheat grain morphology, the current research was conducted to develop a phenotyping method based on digital imaging to capture the grain morphology in bread wheat cultivars of Pakistan and to characterize variation in grain morphology in Pakistani wheat cultivars and their association with TaCWi genes. In this research 64 wheat cultivars were analysed by digital imaging approach. The images were captured using a flatbed scanner with a transparency adapter, and the imaging software image J 1.27 was used for analysis. The cultivars used in this study showed larger variation in grain morphology in more than fifteen descriptors. Measured dimensions included grain area, perimeters, height, width, circularity and solidity. Minimum grain area was showed by the T9 and maximum grain area was shown by the Pirsabak-2013, minimum grain perimeter was showed by the T9 and maximum grain perimeter was showed by the Barsat. Similarly, maximum grain area was exhibited by the Markaz-2019 and minimum grain area was shown by the C-271. Maximum grain perimeter was showed by the PARI-73 and minimum grain perimeter was showed by the C-271 respectively, under well-watered (WW) conditions. The coefficient of determination was also higher indicating the reliability and consistency of digital imaging method. Digital imaging allowed many aspects of development, functions, traits and health to be monitored and traced in ways previously unattainable. Our results are encouraging that large image data sets of grains can be processed by high throughput and accurate manner.

Publisher

Research Square Platform LLC

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