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+has+emerged+as+a+key+driver+of+precision+agriculture%252C+facilitating+enhanced+crop+productivity%252C+optimized+resource+use%252C+farm+sustainability%252C+and+informed+decision-making.+Also%252C+the+expansion+of+genome+sequencing+technology+has+greatly+increased+crop+genomic+resources%252C+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%252C+high-dimensional+datasets.+In+this+work%252C+we+propose+a+new+LSTM+autoencoder-based+model+for+barley+genotype-to-phenotype+prediction%252C+specifically+for+flowering+time+and+grain+yield+estimation%252C+which+could+potentially+help+optimize+yields+and+management+practices.+Our+model+outperformed+the+other+baseline+methods%252C+demonstrating+its+potential+in+handling+complex+high-dimensional+agricultural+datasets+and+enhancing+crop+phenotype+prediction+performance.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.a78c3ecf9d0f91c556904b26df650d34%3frik%3dUWYmZGMKBD66JA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Lstm+Autoencoder+Network+Model+EEG&simid=608002508000220075&FORM=IRPRST&ck=FFF04417F3DD14F2B541CA41A61780D3&selectedIndex=34&itb=0)
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