Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data
Accurate pre-harvest prediction of yield and quality (tenderometric reading, TR) is crucial for the processing pea industry due to a narrow optimal harvest window. Machine learning (ML) models offer potential, but their real-world utility depends on their performance stability across different years...
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004381 |
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| author | Michele Croci Manuele Ragazzi Alessandro Grassi Giorgio Impollonia Stefano Amaducci |
| author_facet | Michele Croci Manuele Ragazzi Alessandro Grassi Giorgio Impollonia Stefano Amaducci |
| author_sort | Michele Croci |
| collection | DOAJ |
| description | Accurate pre-harvest prediction of yield and quality (tenderometric reading, TR) is crucial for the processing pea industry due to a narrow optimal harvest window. Machine learning (ML) models offer potential, but their real-world utility depends on their performance stability across different years. This ability, known as temporal transferability, is often overestimated by standard validation methods, creating a gap between theoretical performance and operational reliability. This study aims to rigorously quantify this temporal transferability gap for both pea yield and TR prediction. Four ML algorithms (RF, XGBoost, GPR, SVMr) were evaluated using Sentinel-2 and ERA5-Land data from 2018 to 2024 in northern Italy. A comparison was made between a standard group-based cross-validation (LOGOCV) and a temporally rigorous Leave-One-Year-Out Cross-Validation (LOYOCV). For yield prediction, ML models outperformed a baseline (NullModel) under LOGOCV (SVMr nRMSE = 18.4 %), but performance degraded significantly under LOYOCV, revealing a clear transferability gap. TR prediction was more challenging while RF showed promising results in LOGOCV (nRMSE = 22.1 %), all ML models were outperformed by the NullModel in the more realistic LOYOCV scenario. The findings highlight a critical temporal transferability gap, especially for the TR parameter, limiting the current operational readiness of standard ML models. It is recommended that future work focus on more robust approaches, such as models designed for temporal data (e.g., RNNs, Transformers) and higher-resolution data, to bridge the gap towards reliable real-world application. |
| format | Article |
| id | doaj-art-7f70bcccce6647369692e47bdb4273bd |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-7f70bcccce6647369692e47bdb4273bd2025-08-20T03:35:47ZengElsevierSmart Agricultural Technology2772-37552025-12-011210120710.1016/j.atech.2025.101207Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land dataMichele Croci0Manuele Ragazzi1Alessandro Grassi2Giorgio Impollonia3Stefano Amaducci4Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore di Piacenza, via Emilia Parmense, 84, 29122 Piacenza, PC, Italy; Corresponding author.Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore di Piacenza, via Emilia Parmense, 84, 29122 Piacenza, PC, ItalyConsorzio Casalasco Del Pomodoro, Strada provinciale, 32, 26036 Rivarolo del Re, CR, ItalyDepartment of Sustainable Crop Production, Università Cattolica del Sacro Cuore di Piacenza, via Emilia Parmense, 84, 29122 Piacenza, PC, ItalyDepartment of Sustainable Crop Production, Università Cattolica del Sacro Cuore di Piacenza, via Emilia Parmense, 84, 29122 Piacenza, PC, ItalyAccurate pre-harvest prediction of yield and quality (tenderometric reading, TR) is crucial for the processing pea industry due to a narrow optimal harvest window. Machine learning (ML) models offer potential, but their real-world utility depends on their performance stability across different years. This ability, known as temporal transferability, is often overestimated by standard validation methods, creating a gap between theoretical performance and operational reliability. This study aims to rigorously quantify this temporal transferability gap for both pea yield and TR prediction. Four ML algorithms (RF, XGBoost, GPR, SVMr) were evaluated using Sentinel-2 and ERA5-Land data from 2018 to 2024 in northern Italy. A comparison was made between a standard group-based cross-validation (LOGOCV) and a temporally rigorous Leave-One-Year-Out Cross-Validation (LOYOCV). For yield prediction, ML models outperformed a baseline (NullModel) under LOGOCV (SVMr nRMSE = 18.4 %), but performance degraded significantly under LOYOCV, revealing a clear transferability gap. TR prediction was more challenging while RF showed promising results in LOGOCV (nRMSE = 22.1 %), all ML models were outperformed by the NullModel in the more realistic LOYOCV scenario. The findings highlight a critical temporal transferability gap, especially for the TR parameter, limiting the current operational readiness of standard ML models. It is recommended that future work focus on more robust approaches, such as models designed for temporal data (e.g., RNNs, Transformers) and higher-resolution data, to bridge the gap towards reliable real-world application.http://www.sciencedirect.com/science/article/pii/S2772375525004381Yield predictionQuality predictionSentinel-2Machine learningTemporal transferability |
| spellingShingle | Michele Croci Manuele Ragazzi Alessandro Grassi Giorgio Impollonia Stefano Amaducci Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data Smart Agricultural Technology Yield prediction Quality prediction Sentinel-2 Machine learning Temporal transferability |
| title | Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data |
| title_full | Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data |
| title_fullStr | Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data |
| title_full_unstemmed | Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data |
| title_short | Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data |
| title_sort | assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using sentinel 2 and era5 land data |
| topic | Yield prediction Quality prediction Sentinel-2 Machine learning Temporal transferability |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004381 |
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