Abstract
Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource management, and sustainable farming practices. Also, the expansion of genome sequencing technology has greatly increased crop genomic resources, offering deeper insights into genetic variation and enhancing desirable crop traits for better performance across various environments. Machine learning (ML) and deep learning (DL) algorithms are gaining traction for genotype-to-phenotype prediction, due to their excellence in capturing complex interactions within large, high-dimensional datasets. In this work, we present a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically targeting flowering time and grain yield estimation. Our model outperformed the other baseline methods, highlighting its effectiveness in handling complex, high-dimensional agricultural datasets and enhancing the accuracy of crop phenotype prediction predictions. This approach has the potential to optimize crop yields and improve management practices.
