Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp (Cannabis sativa L.) Using Artificial Intelligence Methods
| cris.lastimport.scopus | 2025-10-23T06:53:55Z | |
| cris.virtual.author-orcid | 0000-0003-0000-6157 | |
| cris.virtual.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtual.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtualsource.author-orcid | 5e10caab-6ff8-471e-83cf-04cdbe8885b6 | |
| cris.virtualsource.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtualsource.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| dc.abstract.en | 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. | |
| dc.affiliation | Wydział Inżynierii Środowiska i Inżynierii Mechanicznej | |
| dc.affiliation.institute | Katedra Inżynierii Biosystemów | |
| dc.contributor.author | Sieracka, Dominika | |
| dc.contributor.author | Zaborowicz, Maciej | |
| dc.contributor.author | Frankowski, Jakub | |
| dc.date.access | 2025-05-27 | |
| dc.date.accessioned | 2025-08-28T10:06:25Z | |
| dc.date.available | 2025-08-28T10:06:25Z | |
| dc.date.copyright | 2023-05-20 | |
| dc.date.issued | 2023 | |
| dc.description.abstract | <jats:p>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.</jats:p> | |
| dc.description.accesstime | at_publication | |
| dc.description.bibliography | il., bibliogr. | |
| dc.description.finance | publication_nocost | |
| dc.description.financecost | 0,00 | |
| dc.description.if | 3,3 | |
| dc.description.number | 5 | |
| dc.description.points | 140 | |
| dc.description.version | final_published | |
| dc.description.volume | 13 | |
| dc.identifier.doi | 10.3390/agriculture13051097 | |
| dc.identifier.issn | 2077-0472 | |
| dc.identifier.uri | https://sciencerep.up.poznan.pl/handle/item/4473 | |
| dc.identifier.weblink | http://www.mdpi.com/2077-0472/13/5/1097 | |
| dc.language | en | |
| dc.relation.ispartof | Agriculture (Switzerland) | |
| dc.relation.pages | art. 1097 | |
| dc.rights | CC-BY | |
| dc.sciencecloud | send | |
| dc.share.type | OPEN_JOURNAL | |
| dc.source | test | |
| dc.subject.en | neural modeling | |
| dc.subject.en | artificial neural networks | |
| dc.subject.en | sensitivity analysis | |
| dc.subject.en | hemp cultivation | |
| dc.subject.en | seed material | |
| dc.title | Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp (Cannabis sativa L.) Using Artificial Intelligence Methods | |
| dc.title.volume | Special Issue Big Data Analytics and Machine Learning for Smart Agriculture | |
| dc.type | JournalArticle | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 5 | |
| oaire.citation.volume | 13 |