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Identifying key traits for heat stress tolerance in wheat using machine learning | ||
Iranian Journal of Genetics and Plant Breeding | ||
دوره 13، شماره 1 - شماره پیاپی 25، تیر 2024، صفحه 61-84 اصل مقاله (2.55 M) | ||
نوع مقاله: Research paper | ||
شناسه دیجیتال (DOI): 10.30479/ijgpb.2025.20893.1378 | ||
نویسندگان | ||
Mehdi Zahravi* 1؛ Nazanin Amirbakhtiar1؛ Yousef Arhsad1؛ Javad Mahdavimajd2 | ||
1Department of Genetic Research, Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. | ||
2Agricultural and Natural Resources Research Center of Khuzestan, Agricultural Research, Education and Extension Organization (AREEO), Ahwaz, Iran. | ||
تاریخ دریافت: 19 شهریور 1403، تاریخ بازنگری: 30 آذر 1403، تاریخ پذیرش: 27 دی 1403 | ||
چکیده | ||
This study aimed to investigate the effectiveness of machine learning techniques in identifying and prioritizing key traits associated with heat stress tolerance in wheat. Two datasets comprising 203 and 236 wheat genotypes, previously evaluated under normal and heat stress conditions, were analyzed. Machine learning algorithms, including k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), were employed to model the relationships between traits and grain weight of five spikes under heat stress. Results indicated that SVM and ANN models exhibited superior performance in predicting the target trait, with R-squared values approaching 1.0. Correlation analysis and dendrogram analysis highlighted distinct patterns in trait relationships under normal and stress conditions, emphasizing the importance of considering environmental context when studying trait interactions. The analysis of feature importance consistently revealed traits such as the number of grains per spike, days to heading, and 100-grain weight as key characteristics, repeatedly highlighted across different algorithmic approaches, underscoring their fundamental role in heat stress tolerance. The identified key traits can serve as potential targets for genetic manipulation or selection, contributing to the development of heat-tolerant wheat cultivars. The findings of this study highlight the efficacy of machine learning in expediting the breeding of heat-tolerant wheat cultivars. | ||
کلیدواژهها | ||
Artificial Neural Network (ANN)؛ Precision Breeding؛ Predictive Modeling؛ Support Vector Machines (SVM)؛ Trait Importance | ||
عنوان مقاله [English] | ||
شناسایی صفات کلیدی برای تحمل به تنش گرمایی در گندم با استفاده از یادگیری ماشین | ||
نویسندگان [English] | ||
مهدی زهراوی1؛ نازنین امیربختیار1؛ یوسف ارشد1؛ جواد مهدوی مجد2 | ||
1سازمان تحقیقات، آموزش و ترویج کشاورزی، مؤسسه تحقیقات اصلاح و تهیه نهال و بذر، کرج، ایران. | ||
2سازمان تحقیقات، آموزش و ترویج کشاورزی، مرکز تحقیقات کشاورزی و منابع طبیعی خوزستان، اهواز، ایران. | ||
چکیده [English] | ||
این پژوهش با هدف بررسی کارایی روشهای یادگیری ماشین در شناسایی و اولویتبندی صفات کلیدی مرتبط با تحمل به تنش گرما در گندم انجام شد. دو مجموعه داده شامل 203 و 236 ژنوتیپ گندم که پیشتر در شرایط تنش گرما ارزیابی شده بودند، مورد تحلیل قرار گرفت. الگوریتمهای یادگیری ماشین شامل k -نزدیکترین همسایه (KNN)، ماشین بردار پشتیبان (SVM)، جنگل تصادفی (RF) و شبکههای عصبی مصنوعی (ANN) برای مدلسازی روابط بین صفات ارزیابی شده و وزن دانه پنج سنبله تحت تنش گرما به کار گرفته شدند. نتایج نشان داد که مدلهای SVM و ANN با مقادیر R-squared نزدیک به یک، عملکرد بهتری در پیشبینی صفت هدف داشتند. تحلیل اهمیت ویژگیها مبتنی بر پیادهسازی مدلها، در اکثر حالات صفات تعداد دانه در سنبله، تعداد روز تا ظهور سنبله و وزن صد دانه را به عنوان عوامل کلیدی موثر بر تحمل به تنش گرما شناسایی کرد. علاوه بر این، نتایج تجزیهها، پیچیدگی روابط بین صفات تحت تنش گرما را برجسته نمود، که نشاندهنده نیاز به تکنیکهای تحلیلی پیشرفته برای درک کامل مکانیسمهای تحمل به گرما است. تحلیل همبستگی و تحلیل دندروگرام، الگوهای متمایزی در روابط بین صفات تحت شرایط عادی و تنش نشان داد، که تأکید بر اهمیت در نظر گرفتن زمینه محیطی هنگام مطالعه روابط بین صفات دارد. یافتههای این پژوهش کارایی یادگیری ماشین را در تسریع روند اصلاح ارقام گندم برای تحمل به تنش گرما را نشان داد. صفات کلیدی شناساییشده میتوانند بهعنوان اهداف بالقوه برای اصلاح ژنتیکی یا انتخاب، جهت توسعه ارقام گندم متحمل به تنش گرما استفاده شوند. | ||
کلیدواژهها [English] | ||
اهمیت صفات, مدلسازی پیشبینی, اصلاح نباتات دقیق, ماشین بردار پشتیبان (SVM), شبکههای عصبی مصنوعی(ANN) | ||
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