Machine learning approaches for tomato crop yield prediction in precision agriculture

Detalhes bibliográficos
Autor(a) principal: Suescún, María Fernanda Restrepo
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/130704
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
id RCAP_cae5716fb6a6a0be07c8511dac1eb037
oai_identifier_str oai:run.unl.pt:10362/130704
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Machine learning approaches for tomato crop yield prediction in precision agricultureYield predictionTomatoAgricultureMachine learningEnsemble learningInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe objective of this project was to apply ML techniques to predict processing tomato crop yield given information on soil properties, weather conditions, and applied fertilizers. Besides being robust enough for predicting tomato productivity, the model needed to be interpretable and transparent for the business. The models assessed were Decision Trees Regression, ensemble bagging models like Random Forest Regression, and boosting techniques like Gradient Boosting Regression, and Support Vector Regression. Overall, Gradient Boosting and Support Vector models presented the best performance. For improving the predictive power, we combined the predictions of our two best models into a stacked approach with a Ridge Regression as the final model. The generalization error of the final chosen model on new data was 9.02 ton/ha for the MAE metric, 9.5% for the MAPE, and 13.5 ton/ha for the RMSE. This means that our model can predict tomato crop yield with an approximate error of 9 ton/ha. Even though our final model was complex and not intrinsically interpretable, we were able to apply model-agnostic interpretation methods like the SHAP summary plot to better understand the feature importance and feature effects, and the Accumulated Local Effects (ALE) plot, to explain how features influence the outcome of the model on average. In general, the objectives of the project were accomplished and the company was satisfied with the result of the model and its interpretation.Pinheiro, Flávio Luís PortasRUNSuescún, María Fernanda Restrepo2022-01-12T16:12:02Z2021-12-202021-12-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/130704TID:202944638enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:09:21Zoai:run.unl.pt:10362/130704Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:51.889769Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Machine learning approaches for tomato crop yield prediction in precision agriculture
title Machine learning approaches for tomato crop yield prediction in precision agriculture
spellingShingle Machine learning approaches for tomato crop yield prediction in precision agriculture
Suescún, María Fernanda Restrepo
Yield prediction
Tomato
Agriculture
Machine learning
Ensemble learning
title_short Machine learning approaches for tomato crop yield prediction in precision agriculture
title_full Machine learning approaches for tomato crop yield prediction in precision agriculture
title_fullStr Machine learning approaches for tomato crop yield prediction in precision agriculture
title_full_unstemmed Machine learning approaches for tomato crop yield prediction in precision agriculture
title_sort Machine learning approaches for tomato crop yield prediction in precision agriculture
author Suescún, María Fernanda Restrepo
author_facet Suescún, María Fernanda Restrepo
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Suescún, María Fernanda Restrepo
dc.subject.por.fl_str_mv Yield prediction
Tomato
Agriculture
Machine learning
Ensemble learning
topic Yield prediction
Tomato
Agriculture
Machine learning
Ensemble learning
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2021
dc.date.none.fl_str_mv 2021-12-20
2021-12-20T00:00:00Z
2022-01-12T16:12:02Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/130704
TID:202944638
url http://hdl.handle.net/10362/130704
identifier_str_mv TID:202944638
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799138071712628736

Registros relacionados