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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| 主要な著者: | , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Elsevier
2025-12-01
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| シリーズ: | Smart Agricultural Technology |
| 主題: | |
| オンライン･アクセス: | http://www.sciencedirect.com/science/article/pii/S2772375525004381 |
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