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

arXiv: https://arxiv.org/abs/2407.16709 · HTML: https://mlsanity.com/paper/2407.16709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [A CNN-RNN Framework for Crop Yield Prediction](https://mlsanity.com/paper/1911.09045/) (2019)
- [Universal Approximation Theorem for Neural Networks](https://mlsanity.com/paper/2102.10993/) (2021)

## Built on by (most cited first, 1 of 1)

- [G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion](https://mlsanity.com/paper/2502.04684/) (2025)

## Abstract (arXiv)

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.