IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2023i1p25-d1306031.html
   My bibliography  Save this article

Optimizing Linseed ( Linum usitatissimum L.) Seed Yield through Agronomic Parameter Modeling via Artificial Neural Networks

Author

Listed:
  • Aliakbar Mohammadi Mirik

    (Department of Plant Genetics and Production, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran)

  • Mahdieh Parsaeian

    (Department of Agronomy and Plant Breeding, Shahrood University of Technology, Shahrood P.O. Box 316-36155, Iran)

  • Abbas Rohani

    (Department of Biosystem Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Shaneka Lawson

    (USDA Forest Service, Northern Research Station, Hardwood Tree Improvement and Regeneration Center (HTIRC), PFEN226C, Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47907, USA)

Abstract

Linseed ( Linum usitatissimum L.), a globally cultivated oilseed crop in high demand, is the focal point of our efforts aimed at improving yield production. The achievement of robust yield outcomes relies on the intricate interplay of various agronomic traits. This study, conducted over two years at a research farm in Iran, presents a comprehensive analysis evaluating diverse agronomic characteristics inherent to different linseed cultivars and hybrids. Essential parameters, including days to emergence, days to flowering, plant height, number of branches, number of capsules per plant, number of seeds per capsule, 1000-seed weight, and seed yield per plant, were examined. For predictive insights into seed yield, machine learning techniques, specifically multilayer perceptron (MLP) and multiple linear regression (MLR), were employed. The analysis of contribution percentages for each agronomic variable to linseed seed yield revealed that the number of capsules per plant emerged as the most influential factor, contributing 30.7% among the considered variables. The results indicated the superiority of MLP over MLR, with RMSE and MAPE values equaling 0.062 g/plant and 3.585%, respectively. Additionally, R 2 values for training, validation, and test phases exceeded 0.97. Consequently, MLP served as a merit function in the genetic algorithm (GA), targeting the identification of optimal trait levels to maximize linseed yield. The optimization outcomes demonstrated the potential achievement of a yield of 4.40 g/plant. To attain this performance, a set of agronomic characteristic values was proposed by GA, initiating a discussion on genetic modification possibilities. The findings of this study highlight the remarkable efficacy of machine learning tools, particularly neural networks, when paired with evolutionary optimization techniques such as genetic algorithms. These methodologies prove to be invaluable assets in aiding biotechnologists as they strive to enhance the genetic makeup of products for various applications, providing unwavering reliability and invaluable guidance in the pursuit of genetic modification endeavors.

Suggested Citation

  • Aliakbar Mohammadi Mirik & Mahdieh Parsaeian & Abbas Rohani & Shaneka Lawson, 2023. "Optimizing Linseed ( Linum usitatissimum L.) Seed Yield through Agronomic Parameter Modeling via Artificial Neural Networks," Agriculture, MDPI, vol. 14(1), pages 1-21, December.
    • Handle: RePEc:gam:jagris:v:14:y:2023:i:1:p:25-:d:1306031
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/1/25/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/1/25/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    2. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    3. Mohsen Yoosefzadeh-Najafabadi & Dan Tulpan & Milad Eskandari, 2021. "Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
    4. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ji, Li-Qun, 2015. "An assessment of agricultural residue resources for liquid biofuel production in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 561-575.
    2. Chen, Qiuwen & Ma, Xiaohan & Hu, Jiayu & Zhang, Xiaohong, 2023. "Comparison of comprehensive performance of kiwifruit production in China, Iran, and Italy based on emergy and carbon emissions," Ecological Modelling, Elsevier, vol. 483(C).
    3. Hao Hu & Yun Ren & Hongkui Zhou & Weidong Lou & Pengfei Hao & Baogang Lin & Guangzhi Zhang & Qing Gu & Shuijin Hua, 2024. "Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China," Agriculture, MDPI, vol. 14(8), pages 1-15, August.
    4. Srinivasagan N. Subhashree & C. Igathinathane & Adnan Akyuz & Md. Borhan & John Hendrickson & David Archer & Mark Liebig & David Toledo & Kevin Sedivec & Scott Kronberg & Jonathan Halvorson, 2023. "Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review," Agriculture, MDPI, vol. 13(2), pages 1-30, February.
    5. Jiménez, Daniel & Cock, James & Jarvis, Andy & Garcia, James & Satizábal, Héctor F. & Damme, Patrick Van & Pérez-Uribe, Andrés & Barreto-Sanz, Miguel A., 2011. "Interpretation of commercial production information: A case study of lulo (Solanum quitoense), an under-researched Andean fruit," Agricultural Systems, Elsevier, vol. 104(3), pages 258-270, March.
    6. Hossein Jargan & Abbas Rohani & Armaghan Kosari-Moghaddam, 2022. "Application of modeling techniques for energy analysis of fruit production systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2616-2639, February.
    7. Kelvin López-Aguilar & Adalberto Benavides-Mendoza & Susana González-Morales & Antonio Juárez-Maldonado & Pamela Chiñas-Sánchez & Alvaro Morelos-Moreno, 2020. "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    8. Jules F. Cacho & Jeremy Feinstein & Colleen R. Zumpf & Yuki Hamada & Daniel J. Lee & Nictor L. Namoi & DoKyoung Lee & Nicholas N. Boersma & Emily A. Heaton & John J. Quinn & Cristina Negri, 2023. "Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning," Energies, MDPI, vol. 16(10), pages 1-16, May.
    9. Bazrafshan, Ommolbanin & Ehteram, Mohammad & Moshizi, Zahra Gerkaninezhad & Jamshidi, Sajad, 2022. "Evaluation and uncertainty assessment of wheat yield prediction by multilayer perceptron model with bayesian and copula bayesian approaches," Agricultural Water Management, Elsevier, vol. 273(C).
    10. Elahi, Ehsan & Weijun, Cui & Jha, Sunil Kumar & Zhang, Huiming, 2019. "Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability," Energy, Elsevier, vol. 183(C), pages 191-204.
    11. Zhiwei Tian & Ang Gao & Wei Ma & Huanyu Jiang & Dongping Cao & Weizi Wang & Jianping Qian & Lijia Xu, 2024. "Modeling the Mechanical Properties of Root–Substrate Interaction with a Transplanter Using Artificial Neural Networks," Agriculture, MDPI, vol. 14(5), pages 1-12, April.
    12. Javad Zare Derakhshan & Saeed Firouzi & Armaghan Kosari-Moghaddam, 2022. "Energy audit of tobacco production agro-system based on different farm size levels in northern Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2715-2735, February.
    13. Renato Domiciano Silva Rosado & Cosme Damião Cruz & Leiri Daiane Barili & José Eustáquio de Souza Carneiro & Pedro Crescêncio Souza Carneiro & Vinicius Quintão Carneiro & Jackson Tavela da Silva & Moy, 2020. "Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars," Agriculture, MDPI, vol. 10(12), pages 1-11, December.
    14. Kuruguntu Mohan Krithika & Nachimuthu Maheswari & Manickam Sivagami, 2022. "Models for feature selection and efficient crop yield prediction in the groundnut production," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 68(3), pages 131-141.
    15. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    16. Jalilov, Shokhrukh-Mirzo & Rahman, Wakilur & Palash, Salauddin & Jahan, Hasneen & Mainuddin, Mohammed & Ward, Frank A., 2022. "Exploring strategies to control the cost of food security: Evidence from Bangladesh," Agricultural Systems, Elsevier, vol. 196(C).
    17. Omolola M. Adisa & Joel O. Botai & Abiodun M. Adeola & Abubeker Hassen & Christina M. Botai & Daniel Darkey & Eyob Tesfamariam, 2019. "Application of Artificial Neural Network for Predicting Maize Production in South Africa," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    18. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    19. Naseri, Hakim & Parashkoohi, Mohammad Gholami & Ranjbar, Iraj & Zamani, Davood Mohammad, 2021. "Energy-economic and life cycle assessment of sugarcane production in different tillage systems," Energy, Elsevier, vol. 217(C).
    20. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2023:i:1:p:25-:d:1306031. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.