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CC BY-NC-SA V4.0, Open Access
Preprint
LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction
ArXiv.org
Cornell University
2024
Abstract
Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource use, farm sustainability, and informed decision-making. Also, the expansion of genome sequencing technology has greatly increased crop genomic resources, deepening our understanding of genetic variation and enhancing desirable crop traits to optimize performance in various environments. There is increasing interest in using machine learning (ML) and deep learning (DL) algorithms for genotype-to-phenotype prediction due to their excellence in capturing complex interactions within large, high-dimensional datasets. In this work, we propose a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically for flowering time and grain yield estimation, which could potentially help optimize yields and management practices. Our model outperformed the other baseline methods, demonstrating its potential in handling complex high-dimensional agricultural datasets and enhancing crop phenotype prediction performance.
Details
- Title
- LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction
- Authors/Creators
- Guanjin WangJunyu XuanPenghao WangChengdao LiJie Lu
- Publication Details
- ArXiv.org
- Publisher
- Cornell University
- Identifiers
- 991005689561107891
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Medical, Molecular and Forensic Sciences
- Language
- English
- Resource Type
- Preprint
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