کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5913193 1162171 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Machine Learning for High-Throughput Stress Phenotyping in Plants
ترجمه فارسی عنوان
یادگیری ماشین برای فنوتایپ در استرس با کارایی بالا در گیاهان
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک دانش گیاه شناسی
چکیده انگلیسی
Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Trends in Plant Science - Volume 21, Issue 2, February 2016, Pages 110-124
نویسندگان
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