2024-07-18
【學術亮點】用於提高農作物生產力的目標感知產量預測 (TAYP) 模型
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【學術亮點】用於提高農作物生產力的目標感知產量預測 (TAYP) 模型
AI core Technology: Advanced Research and Resource Integration Platform or AI Technology【Department of Computer Science and Engineering / Yen-Jen Chang / Professor】
核心技術：AI核心技術之進階研究與資源整合平台【資訊工程學系張延任教授】
上架日期：2024/3/14
AI core Technology: Advanced Research and Resource Integration Platform or AI Technology【Department of Computer Science and Engineering / Yen-Jen Chang / Professor】
核心技術：AI核心技術之進階研究與資源整合平台【資訊工程學系張延任教授】
| 論文篇名 | 英文：Target-Aware Yield Prediction (TAYP) Model Used to Improve Agriculture Crop Productivity 中文：用於提高農作物生產力的目標感知產量預測 (TAYP) 模型 |
| 期刊名稱 | IEEE Transactions on Geoscience and Remote Sensing |
| 發表年份, 卷數, 起迄頁數 | 2024, 62, 5404111 |
| 作者 | Yen-Jen Chang(張延任); Ming-Hsin Lai; Chien-Ho Wang*; Yu-Shun Huang; Jason Lin |
| DOI | 10.1109/TGRS.2024.3376078 |
| 中文摘要 | 由於水稻是最重要的糧食作物，其產量預測對糧食政策和農民收入有著至關重要的影響。在本文中，我們提出了一種新的水稻產量預測模型，稱為目標感知產量預測（TAYP）模型，可以有效提高產量預測的準確性。所提出的 TAYP 模型是一個基於長短期記憶（LSTM）的網絡，其中我們透過引入目標報酬率來修改損失函數。與傳統的與目標產量無關的損失函數不同，我們的設計可以使預測模型對目標產量敏感，從而提高產量預測的準確性。為了測試 TAYP 模型，我們使用台灣農業研究所的水稻資料集，該資料集由無人機收集的多光譜植被指數組成。實驗結果表明，TAYP模型在各種評估標準上均優於相關工作。與傳統的LSTM模型相比，TAYP模型的均方根誤差（RMSE）和R平方分別提高了6.1%和13.0%，同時精度從89%提高到95%。特別是TAYP的Kappa值為0.82，與實際測量幾乎完美吻合。顯然，所提出的 TAYP 模型可以顯著提高水稻產量預測的準確性，並有可能成為提高農業生產力的有用工具。 |
| 英文摘要 | Because rice is the most important food crop, its yield prediction has a critical impact on the food policy and farmer income. In this article, we propose a new yield prediction model for rice, called target-aware yield prediction (TAYP) model that can effectively improve the accuracy of yield prediction. The proposed TAYP model is a long short-term memory (LSTM)-based network, in which we modify the loss function by introducing the target yield. Unlike the traditional loss function that is independent of the target yield, our design can make the prediction model sensitive to the target yield such that the accuracy of yield prediction is increased. To test the TAYP model, we use a rice dataset from Taiwan Agricultural Research Institute, which consists of multispectral vegetation indexes collected by drones. The experimental results show that the TAYP model performs better than the related works on various evaluation criteria. Compared to the traditional LSTM model, the TAYP model improves the root-mean-squared error (RMSE) and R -squared by 6.1% and 13.0%, respectively, while increasing accuracy from 89% to 95%. In particular, the Kappa value of TAYP is 0.82, which is almost perfect agreement with the real measurement. It is clear that the proposed TAYP model can make significant accuracy improvement to the rice yield prediction and has the potential to be a useful tool for improving agricultural productivity. |
| 發表成果與AI計畫研究主題相關性 | 本論文提出了一種新的稻米產量預測模型，名為目標感知產量預測（TAYP）模型，這是一個基於長短期記憶（LSTM）的網路，通過引入目標產量來修改損失函數。該模型使用來自台灣農業試驗所的無人機多光譜植被指數數據集進行測試，實驗結果顯示TAYP模型在各種評估標準上均優於相關文獻。 |
