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Forecasting Mango Crop Yield in Miyazaki using Deep Learning
https://doi.org/10.34481/0002000820
https://doi.org/10.34481/000200082038b55aff-5f62-4cd8-902d-c34359b31811
| 名前 / ファイル | ライセンス | アクション |
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本文 (1.2 MB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||||||||||||
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| 公開日 | 2024-10-23 | |||||||||||||||
| タイトル | ||||||||||||||||
| タイトル | Forecasting Mango Crop Yield in Miyazaki using Deep Learning | |||||||||||||||
| 言語 | en | |||||||||||||||
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| 言語 | eng | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Deep learning network | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Long Short-Term Memory | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Forecasting | |||||||||||||||
| キーワード | ||||||||||||||||
| 言語 | en | |||||||||||||||
| 主題Scheme | Other | |||||||||||||||
| 主題 | Miyazaki mangoes | |||||||||||||||
| 資源タイプ | ||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||
| 資源タイプ | departmental bulletin paper | |||||||||||||||
| ID登録 | ||||||||||||||||
| ID登録 | 10.34481/0002000820 | |||||||||||||||
| ID登録タイプ | JaLC | |||||||||||||||
| アクセス権 | ||||||||||||||||
| アクセス権 | open access | |||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||
| 著者 |
Izzati Ulinnuha, Arini Robbil
× Izzati Ulinnuha, Arini Robbil
× 坂本, 眞人× 舛屋, 賢× 河村, 隆介
WEKO
5692
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| 抄録 | ||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||
| 内容記述 | Mangoes from Miyazaki Prefecture are known for their high-quality, which are famous for fully ripeness, rich flavor, and high price. The premium mango is an important agricultural product of the region that must be able to survive in different seasons. This adaptation ensures consistency in quality and yield but introduces complexities in forecasting due to the sensitive interplay of climatic factors. This study explores the use of Long Short-Term Memory, LSTM deep learning models to improve forecasting mango crop yield in Miyazaki Prefecture. By analyzing extensive historical data on climate and past yields from 2002 to 2019, the LSTM model has been designed to analyze the complex factors affecting crop production. The results demonstrate that the LSTM models can effectively incorporate multiple variables that influence agricultural production, offering a robust tool for farmers and agricultural stakeholders to optimize their crop yield predictions and management practices. The strongest factor influencing crop yield is the cultivation area, with an R-squared value of 0.9433. For further analysis, forecasting Miyazaki mango crop yield has been performed using LSTM deep learning with a univariate approach, focusing on the correlation between cultivation area and mango crop yield. The best result in univariate forecasting is obtained with the number of batch size of 2 and root mean square error, RMSE in mango crop yield is 75.803. To improve these results, it is recommended to add more data and relevant features and optimize the settings which minimize the RMSE. The use of the LSTM model demonstrates the potential of deep learning techniques in forecasting agricultural production. | |||||||||||||||
| 言語 | en | |||||||||||||||
| bibliographic_information |
ja : 宮崎大学工学部紀要 en : Memoirs of Faculty of Engineering, University of Miyazaki 巻 53, p. 79-82, 発行日 2024-10-23 |
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| 出版者 | ||||||||||||||||
| 出版者 | 宮崎大学工学部 | |||||||||||||||
| 言語 | ja | |||||||||||||||
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| 出版者 | Faculty of Engineering, University of Miyazaki | |||||||||||||||
| 言語 | en | |||||||||||||||
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| 収録物識別子タイプ | PISSN | |||||||||||||||
| 収録物識別子 | 05404924 | |||||||||||||||
| item_10002_source_id_11 | ||||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||||
| 収録物識別子 | AA00732558 | |||||||||||||||
| 出版タイプ | ||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||
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Izzati Ulinnuha, Arini Robbil, 坂本, 眞人, 舛屋, 賢, 河村, 隆介, 2024, Forecasting Mango Crop Yield in Miyazaki using Deep Learning: 宮崎大学工学部, 79–82 p.
