Recent advances in the field of genomic trait prediction has paved the way for developing futuristic plant breeding programs. The objective of our study is to predict a single or multiple traits of rapeseed (Brassica napus) based on the RNA sequence data. We analyzed 12 different traits of rapeseed and evaluated how their pair-wise correlation impact on the yield production. Further, for predicting single or multi-traits of rapeseed, four state-of-art machine learning (ML) models, namely – Lasso Regression (Lasso), Random Forest (RF), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) were evaluated. For both single and multi-trait predictions, our RF and SVM models performed most consistently, where the lowest mean squared error was achieved by RF (0.045 and 0.016 for the single and multi-trait prediction respectively). A comparative analysis with related works showed the potentiality of our model for future multi-modal model development. Future study in this context could comprise of evaluating our models with other transcriptome dataset from related crops or deep learning-based methods for better outcomes.
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