Hybrid Deep Learning-based Models for Crop Yield Prediction
<p dir="ltr">Predicting crop yield is a complex task since it depends on multiple factors. Although many models have been developed so far in the literature, the performance of current models is not satisfactory, and hence, they must be improved. In this study, we developed deep lear...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2022
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513520215785472 |
|---|---|
| author | Alexandros Oikonomidis (12050497) |
| author2 | Cagatay Catal (6897842) Ayalew Kassahun (8849540) |
| author2_role | author author |
| author_facet | Alexandros Oikonomidis (12050497) Cagatay Catal (6897842) Ayalew Kassahun (8849540) |
| author_role | author |
| dc.creator.none.fl_str_mv | Alexandros Oikonomidis (12050497) Cagatay Catal (6897842) Ayalew Kassahun (8849540) |
| dc.date.none.fl_str_mv | 2022-01-22T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1080/08839514.2022.2031823 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Hybrid_Deep_Learning-based_Models_for_Crop_Yield_Prediction/25441756 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Crop Yield Prediction Hybrid Deep Learning-based Models Convolutional Neural Networks (CNN) Deep Neural Networks (DNN) CNN-XGBoost Recurrent Neural Networks (RNN) |
| dc.title.none.fl_str_mv | Hybrid Deep Learning-based Models for Crop Yield Prediction |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Predicting crop yield is a complex task since it depends on multiple factors. Although many models have been developed so far in the literature, the performance of current models is not satisfactory, and hence, they must be improved. In this study, we developed deep learning-based models to evaluate how the underlying algorithms perform with respect to different performance criteria. The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN)-Deep Neural Networks (DNN), CNN-XGBoost, CNN-Recurrent Neural Networks (RNN), and CNN-Long Short Term Memory (LSTM). For the case study, we performed experiments on a public soybean dataset that consists of 395 features including weather and soil parameters and 25,345 samples. The results showed that the hybrid CNN-DNN model outperforms other models, having an RMSE equal to 0.266, an MSE of 0.071, and an MAE of 0.199. The predictions of the model fit with an R<sup>2</sup> of 0.87. The second-best result was achieved by the XGBoost model, which required less time to execute compared to the other DL-based models.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/08839514.2022.2031823" target="_blank">https://dx.doi.org/10.1080/08839514.2022.2031823</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5c2434f9ef69a93b8f5606d90e483986 |
| identifier_str_mv | 10.1080/08839514.2022.2031823 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25441756 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Hybrid Deep Learning-based Models for Crop Yield PredictionAlexandros Oikonomidis (12050497)Cagatay Catal (6897842)Ayalew Kassahun (8849540)Information and computing sciencesArtificial intelligenceCrop Yield PredictionHybrid Deep Learning-based ModelsConvolutional Neural Networks (CNN)Deep Neural Networks (DNN)CNN-XGBoostRecurrent Neural Networks (RNN)<p dir="ltr">Predicting crop yield is a complex task since it depends on multiple factors. Although many models have been developed so far in the literature, the performance of current models is not satisfactory, and hence, they must be improved. In this study, we developed deep learning-based models to evaluate how the underlying algorithms perform with respect to different performance criteria. The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN)-Deep Neural Networks (DNN), CNN-XGBoost, CNN-Recurrent Neural Networks (RNN), and CNN-Long Short Term Memory (LSTM). For the case study, we performed experiments on a public soybean dataset that consists of 395 features including weather and soil parameters and 25,345 samples. The results showed that the hybrid CNN-DNN model outperforms other models, having an RMSE equal to 0.266, an MSE of 0.071, and an MAE of 0.199. The predictions of the model fit with an R<sup>2</sup> of 0.87. The second-best result was achieved by the XGBoost model, which required less time to execute compared to the other DL-based models.</p><h2>Other Information</h2><p dir="ltr">Published in: Applied Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/08839514.2022.2031823" target="_blank">https://dx.doi.org/10.1080/08839514.2022.2031823</a></p>2022-01-22T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1080/08839514.2022.2031823https://figshare.com/articles/journal_contribution/Hybrid_Deep_Learning-based_Models_for_Crop_Yield_Prediction/25441756CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/254417562022-01-22T03:00:00Z |
| spellingShingle | Hybrid Deep Learning-based Models for Crop Yield Prediction Alexandros Oikonomidis (12050497) Information and computing sciences Artificial intelligence Crop Yield Prediction Hybrid Deep Learning-based Models Convolutional Neural Networks (CNN) Deep Neural Networks (DNN) CNN-XGBoost Recurrent Neural Networks (RNN) |
| status_str | publishedVersion |
| title | Hybrid Deep Learning-based Models for Crop Yield Prediction |
| title_full | Hybrid Deep Learning-based Models for Crop Yield Prediction |
| title_fullStr | Hybrid Deep Learning-based Models for Crop Yield Prediction |
| title_full_unstemmed | Hybrid Deep Learning-based Models for Crop Yield Prediction |
| title_short | Hybrid Deep Learning-based Models for Crop Yield Prediction |
| title_sort | Hybrid Deep Learning-based Models for Crop Yield Prediction |
| topic | Information and computing sciences Artificial intelligence Crop Yield Prediction Hybrid Deep Learning-based Models Convolutional Neural Networks (CNN) Deep Neural Networks (DNN) CNN-XGBoost Recurrent Neural Networks (RNN) |