Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach

Mahdieh Parsaeian, Mohammad Rahimi, Abbas Rohani () and Shaneka S. Lawson
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Mahdieh Parsaeian: Department of Agronomy and Plant Breeding, Shahrood University of Technology, Shahrood 3619995161, Iran
Mohammad Rahimi: Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Abbas Rohani: Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Shaneka S. Lawson: USDA Forest Service, Northern Research Station, Hardwood Tree Improvement and Regeneration Center (HTIRC), Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47906, USA

Agriculture, 2022, vol. 12, issue 10, 1-23

Abstract: Crop seed yield modeling and prediction can act as a key approach in the precision agriculture industry, enabling the reliable assessment of the effectiveness of agro-traits. Here, multiple machine learning (ML) techniques are employed to predict sesame ( Sesamum indicum L.) seed yields (SSY) using agro-morphological features. Various ML models were applied, coupled with the PCA (principal component analysis) method to compare them with the original ML models, in order to evaluate the prediction 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 R 2 values, of 0.99 and 0.91, respectfully. The root-mean-square error (RMSE) obtained using the ML models ranged between 0 and 0.30 t/ha (metric tons/hectare) for the varied modeling process phases. The estimation of the sesame seed yield with the coupled PCA-ML models improved the performance accuracy. According to the k-fold process, we utilized the datasets with the lowest error rates to ensure the continued accuracy of the GPR and RBF models. The sensitivity analysis revealed that the capsule number per plant (CPP), seed number per capsule (SPC), and 1000-seed weight (TSW) were the most significant seed yield determinants.

Keywords: agro-morphological; data-driven; machine learning; seed yield; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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