Hybrid Deep Learning-based Models for Crop Yield Prediction
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2031823 |
| _version_ | 1797641061859852288 |
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| author | Alexandros Oikonomidis Cagatay Catal Ayalew Kassahun |
| author_facet | Alexandros Oikonomidis Cagatay Catal Ayalew Kassahun |
| author_sort | Alexandros Oikonomidis |
| collection | DOAJ |
| description | 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 R2 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. |
| first_indexed | 2024-03-11T13:40:05Z |
| format | Article |
| id | doaj.art-26dfaad3676a4bc49b1ac12812e757ff |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:05Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-26dfaad3676a4bc49b1ac12812e757ff2023-11-02T13:36:38ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2022.20318232031823Hybrid Deep Learning-based Models for Crop Yield PredictionAlexandros Oikonomidis0Cagatay Catal1Ayalew Kassahun2Wageningen University & ResearchQatar UniversityWageningen University & ResearchPredicting 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 R2 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.http://dx.doi.org/10.1080/08839514.2022.2031823 |
| spellingShingle | Alexandros Oikonomidis Cagatay Catal Ayalew Kassahun Hybrid Deep Learning-based Models for Crop Yield Prediction Applied Artificial Intelligence |
| 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 |
| url | http://dx.doi.org/10.1080/08839514.2022.2031823 |
| work_keys_str_mv | AT alexandrosoikonomidis hybriddeeplearningbasedmodelsforcropyieldprediction AT cagataycatal hybriddeeplearningbasedmodelsforcropyieldprediction AT ayalewkassahun hybriddeeplearningbasedmodelsforcropyieldprediction |