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

TL;DR

This paper introduces an LSTM autoencoder-based deep learning model for predicting barley phenotypes from genotypes, demonstrating improved accuracy over baseline methods in estimating flowering time and grain yield.

Contribution

The study presents a novel LSTM autoencoder approach tailored for genotype-to-phenotype prediction in barley, advancing deep learning applications in precision agriculture.

Findings

Model outperforms baseline methods in phenotype prediction

Effective handling of high-dimensional agricultural data

Potential to optimize crop yields and management practices

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.