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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mahdieh Parsaeian & Mohammad Rahimi & Abbas Rohani & Shaneka S. Lawson, 2022. "Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach," Agriculture, MDPI, vol. 12(10), pages 1-23, October.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1739-:d:949592
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    1. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    2. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    3. Vinushi Amaratunga & Lasini Wickramasinghe & Anushka Perera & Jeevani Jayasinghe & Upaka Rathnayake, 2020. "Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, July.
    4. Mousavi-Avval, Seyed Hashem & Shah, Ajay, 2021. "Techno-economic analysis of hydroprocessed renewable jet fuel production from pennycress oilseed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Masako Ikegami & Zijian Wang, 2021. "Does energy aid reduce CO2 emission intensities in developing countries?," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 10(4), pages 343-358, October.
    6. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
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    Cited by:

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    2. Rahimi, Mohammad & Mashhadimoslem, Hossein & Vo Thanh, Hung & Ranjbar, Benyamin & Safarzadeh Khosrowshahi, Mobin & Rohani, Abbas & Elkamel, Ali, 2023. "Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques," Energy, Elsevier, vol. 283(C).
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