Association mapping analysis of oil palm interspecific hybrid populations and predicting phenotypic values via machine learning algorithms

Shin, Min-Gyoung; Ithnin, Maizura; Vu, Wendy; Kamaruddin, Katialisa; Chin, Ting Ngoot; Yaakub, Zulkifli; Chang, Peter L.; Sritharan, Kandha; Nuzhdin, Sergey V.; Singh, Rajinder

doi:10.1111/pbr.12967

Cited by 6 publications

(1 citation statement)

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“…Machine learning (ML) techniques have attracted extensive attention because they can be easily used in fields such as agriculture, chemical, and energy for a variety of applications [29][30][31][32][33][34][35] Consequently, agronomists have shifted to machine learning methods like Artificial Neural Networks (ANNs) and Gaussian Process Regression (GPR) models in recent years [36][37][38][39][40]. ML models are especially effective in agricultural fields and have been used for product image processing [41],…”

Section: Introductionmentioning

confidence: 99%

Towards Modeling and Predicting the Yield of Oilseed Crops: Multi-Machine Learning Techniques Approach

Parsaeian¹,

Rahimi²,

Rohani³

et al. 2022

Preprint

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The modeling and prediction of crop seed yield can be a vital improvement in the precision agriculture industry as it provides reliable assessments of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are established for predicting sesame (Sesamum indicum L.) seed yield (SSY) and incorporating agro-morphological features. Models utilized for coupled PCA-ML (Principal component analysis-Machine Learning) methods were compared with original ML models to evaluate predicted efficiency. The Gaussian process regression (GPR) and Radial basis function neural network (RBF-NN) models exhibited the most accurate SSY predictions with determination coefficients or R2 values of 0.99 and 0.91, respectively. The root-mean-square error (RMSE) for the ML models fluctuated between 0 to 0.30 t/ha (metric tons/hectare) for the varied modeling process phases. Estimation of sesame seed yield with coupled PCA-ML models improved performance accuracy. The K-fold process suggested the utilization of datasets with the lowest error rates to ensure the continued accuracy of GPR and RBF models. Sensitivity analysis revealed the capsule number per plant (CPP), seed number per capsule (SPC), and 1000-seed weight (TSW) were the most significant seed yield determinants.

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