LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction

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.

Plant phenotyping relevance

大麦の開花期・穀粒収量という植物形質を予測するLSTMオートエンコーダモデルを新規開発し、ベースライン比較で性能検証しており、計算的な形質推定法が中心である。

Code and data availability

The paper's barley genotype/phenotype dataset (WCGA, Murdoch University) is described but no public deposit or availability URL is given. The two GitHub links cited are generic third-party libraries (an LSTM autoencoder reference implementation and XGBoost), not authors' code or paper-specific assets. No trained models

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