![LSTM neural network design based on [29]. | Download Scientific Diagram](https://www.researchgate.net/publication/375848246/figure/fig2/AS:11431281206491149@1700747965288/LSTM-neural-network-architecture-with-2-hidden-layers-with-8-neurons-each_Q320.jpg)
%20has%20emerged%20as%20a%20key%20driver%20of%20precision%20agriculture%2C%20facilitating%20enhanced%20crop%20productivity%2C%20optimized%20resource%20use%2C%20farm%20sustainability%2C%20and%20informed%20decision-making.%20Also%2C%20the%20expansion%20of%20genome%20sequencing%20technology%20has%20greatly%20increased%20crop%20genomic%20resources%2C%20deepening%20our%20understanding%20of%20genetic%20variation%20and%20enhancing%20desirable%20crop%20traits%20to%20optimize%20performance%20in%20various%20environments.%20There%20is%20increasing%20interest%20in%20using%20machine%20learning%20(ML)%20and%20deep%20learning%20(DL)%20algorithms%20for%20genotype-to-phenotype%20prediction%20due%20to%20their%20excellence%20in%20capturing%20complex%20interactions%20within%20large%2C%20high-dimensional%20datasets.%20In%20this%20work%2C%20we%20propose%20a%20new%20LSTM%20autoencoder-based%20model%20for%20barley%20genotype-to-phenotype%20prediction%2C%20specifically%20for%20flowering%20time%20and%20grain%20yield%20estimation%2C%20which%20could%20potentially%20help%20optimize%20yields%20and%20management%20practices.%20Our%20model%20outperformed%20the%20other%20baseline%20methods%2C%20demonstrating%20its%20potential%20in%20handling%20complex%20high-dimensional%20agricultural%20datasets%20and%20enhancing%20crop%20phenotype%20prediction%20performance.)
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