  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.
