Prediction of Protein Content in Pea (Pisum sativum L.) Seeds Using Artificial Neural Networks
| cris.lastimport.scopus | 2025-10-23T06:56:56Z | |
| cris.virtual.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtual.author-orcid | 0000-0003-3721-6473 | |
| cris.virtual.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtualsource.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtualsource.author-orcid | 3fe42726-36c4-478a-818f-a10f72d4a6ef | |
| cris.virtualsource.author-orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| dc.abstract.en | Pea (Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security. | |
| dc.affiliation | Wydział Inżynierii Środowiska i Inżynierii Mechanicznej | |
| dc.affiliation.institute | Katedra Inżynierii Biosystemów | |
| dc.contributor.author | Hara, Patryk | |
| dc.contributor.author | Piekutowska, Magdalena | |
| dc.contributor.author | Niedbała, Gniewko | |
| dc.date.access | 2025-05-26 | |
| dc.date.accessioned | 2025-08-26T09:30:15Z | |
| dc.date.available | 2025-08-26T09:30:15Z | |
| dc.date.copyright | 2022-12-22 | |
| dc.date.issued | 2023 | |
| dc.description.abstract | <jats:p>Pea (Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security.</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 | 1 | |
| dc.description.points | 140 | |
| dc.description.version | final_published | |
| dc.description.volume | 13 | |
| dc.identifier.doi | 10.3390/agriculture13010029 | |
| dc.identifier.issn | 2077-0472 | |
| dc.identifier.uri | https://sciencerep.up.poznan.pl/handle/item/4368 | |
| dc.identifier.weblink | https://www.mdpi.com/2077-0472/13/1/29 | |
| dc.language | en | |
| dc.relation.ispartof | Agriculture (Switzerland) | |
| dc.relation.pages | art. 29 | |
| dc.rights | CC-BY | |
| dc.sciencecloud | send | |
| dc.share.type | OPEN_JOURNAL | |
| dc.subject.en | artificial neural networks | |
| dc.subject.en | multiple linear regression | |
| dc.subject.en | protein prediction | |
| dc.subject.en | pea | |
| dc.subject.en | sensitivity analysis | |
| dc.subject.en | weather conditions | |
| dc.title | Prediction of Protein Content in Pea (Pisum sativum L.) Seeds Using Artificial Neural Networks | |
| dc.title.volume | Special Issue Digital Innovations in Agriculture | |
| dc.type | JournalArticle | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 1 | |
| oaire.citation.volume | 13 |