Show simple item record

dc.contributor.authorMokhtar, Ali
dc.contributor.authorEl-Ssawy, Wessam
dc.contributor.authorHe, Hongming
dc.contributor.authorAl-Anasari, Nadhir
dc.contributor.authorSammen, Saad Sh
dc.contributor.authorGyasi-Agyei, Yeboah
dc.contributor.authorAbuarab, Mohamed
dc.date.accessioned2022-03-29T02:58:26Z
dc.date.available2022-03-29T02:58:26Z
dc.date.issued2022
dc.identifier.issn1664-462X
dc.identifier.doi10.3389/fpls.2022.706042
dc.identifier.urihttp://hdl.handle.net/10072/413629
dc.description.abstractPrediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherFrontiers Media SA
dc.relation.ispartofpagefrom706042
dc.relation.ispartofjournalFrontiers in Plant Science
dc.relation.ispartofvolume13
dc.subject.fieldofresearchPlant biology
dc.subject.fieldofresearchAquaculture
dc.subject.fieldofresearchCrop and pasture production
dc.subject.fieldofresearchcode3108
dc.subject.fieldofresearchcode300501
dc.subject.fieldofresearchcode3004
dc.subject.keywordsDNN
dc.subject.keywordsdeep learning
dc.subject.keywordsfood safety 2
dc.subject.keywordsmachine learning
dc.subject.keywordsyield prediction
dc.titleUsing Machine Learning Models to Predict Hydroponically Grown Lettuce Yield
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMokhtar, A; El-Ssawy, W; He, H; Al-Anasari, N; Sammen, SS; Gyasi-Agyei, Y; Abuarab, M, Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield, Frontiers in Plant Science, 2022, 13, pp. 706042
dcterms.dateAccepted2022-01-18
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2022-03-24T18:06:14Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2022 Mokhtar, El-Ssawy, He, Al-Anasari, Sammen, Gyasi-Agyei and Abuarab. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
gro.hasfulltextFull Text
gro.griffith.authorGyasi-Agyei, Yeboah


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record