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Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp ( Cannabis sativa L.) Using Artificial Intelligence Methods

Author

Listed:
  • Dominika Sieracka

    (Department of Bioeconomy, Institute of Natural Fibers and Medicinal Plants—National Research Institute, Wojska Polskiego 71B, 60-630 Poznan, Poland)

  • Maciej Zaborowicz

    (Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, Poland)

  • Jakub Frankowski

    (Department of Bioeconomy, Institute of Natural Fibers and Medicinal Plants—National Research Institute, Wojska Polskiego 71B, 60-630 Poznan, Poland)

Abstract

Currently, there is a significant increase in interest in hemp cultivation and hemp products around the world. The hemp industry is a strongly developing branch of the economies of many countries. Short-term forecasting of the hemp seed and grain yield will provide growers and processors with information useful to plan the demand for employees, technical facilities (including appropriately sized drying houses and crop cleaning lines) and means of transport. This will help to optimize inputs and, as a result, increase the income from cultivation. One of the methods of yield prediction is the use of artificial intelligence (AI) methods. Neural modeling proved to be useful in predicting the yield of many plants, which is why work was undertaken to use it also to predict hemp yield. The research was carried out on selected, popular hemp varieties—Białobrzeskie and Henola. Their aim was to identify characteristic factors: climatic, cultivation and agrotechnical, affecting the size and quality of the yield. The collected data allowed the generation of Artificial Neural Network (ANN) models. It has been shown that based on a set of characteristics obtained during the cultivation process, it is possible to create a predictive neural model. Modeling using one output variable, which is seed yield, can be used in short-time prediction of industrial crops, which are gaining more and more importance.

Suggested Citation

  • Dominika Sieracka & Maciej Zaborowicz & Jakub Frankowski, 2023. "Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp ( Cannabis sativa L.) Using Artificial Intelligence Methods," Agriculture, MDPI, vol. 13(5), pages 1-11, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:1097-:d:1151868
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    References listed on IDEAS

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    1. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    2. Jakub Frankowski & Maciej Zaborowicz & Jacek Dach & Wojciech Czekała & Jacek Przybył, 2020. "Biological Waste Management in the Case of a Pandemic Emergency and Other Natural Disasters. Determination of Bioenergy Production from Floricultural Waste and Modeling of Methane Production Using Deep Neural Modeling Methods," Energies, MDPI, vol. 13(11), pages 1-15, June.
    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. Katarzyna Szwedziak & Ewa Polańczyk & Żaneta Grzywacz & Gniewko Niedbała & Wiktoria Wojtkiewicz, 2020. "Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage," Sustainability, MDPI, vol. 12(12), pages 1-14, June.
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    Cited by:

    1. Maciej Zaborowicz & Jakub Frankowski, 2025. "Big Data Analytics and Machine Learning for Smart Agriculture," Agriculture, MDPI, vol. 15(7), pages 1-4, March.

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