2025-04-25
【學術亮點】結合規則基礎之多任務深度學習於高效水稻倒伏分割
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Intelligent Cultivation: Crop digital twin for growth adversity index and disaster damage assessment【Department of Civil Engineering / Ming-Der Yang / Tenured Distinguished Professor】
智慧栽培：數位分身作物生長逆境指標與災損監測【土木工程學系楊明德終身特聘教授】
上架日期2025-04-25
智慧栽培：數位分身作物生長逆境指標與災損監測【土木工程學系楊明德終身特聘教授】
| 論文篇名 | 英文：Rule-Based Multi-Task Deep Learning for Highly Efficient Rice Lodging Segmentation 中文：結合規則基礎之多任務深度學習於高效水稻倒伏分割 |
| 期刊名稱 | Remote Sensing |
| 發表年份, 卷數, 起迄頁數 | 2025, 17(19), no.1505 |
| 作者 | Yang, Ming-Der (楊明德); Tseng, Hsin-Hung |
| DOI | 10.3390/rs17091505 |
| 中文摘要 | 本研究提出了一種基於規則的多任務深度學習方法，透過引入先驗知識來提高利用無人機（UAV）影像進行高效水稻倒伏辨識的效率，從而改進災害調查。多任務學習結合了基於規則的損失函數，並學習最佳的損失函數來訓練符合先驗知識的模型。基於規則和多任務學習優化了基於規則和深度學習網路的整合，並動態調整損失函數模型。最後，邊緣運算可以被部署在邊緣運算主機上，以提高模型效率，實現即時推論。本研究推論了 2019 年拍攝的51張 4096 × 4096 已標記的無人機影像，並計算了混淆矩陣和精度指標。修改後的模型在正常水稻類別中的召回率提高了 13.7%。影響因素可能是不同時期空間解析度的變化和光譜值的差異造成的，這可以透過增加部分 2019 年的影像進行遷移學習以調整學習特徵來解決。深度學習網路的先驗知識可以部署在邊緣運算設備上，透過在推論出的受災農田中進行區域航線規劃來收集高解析度影像，從而提供具有高偵測精確度的有效災害調查工具。 |
| 英文摘要 | This study proposes rule-based multi-task deep learning for highly efficient rice lodging identification by introducing prior knowledge to improve the efficiency of disaster investigation using unmanned aerial vehicle (UAV) images. Multi-task learning combines rule-based loss functions and learns the best loss function to train a model conforming to prior knowledge. Rule-based and multi-task learning optimizes the integration of rule-based and deep learning networks and dynamically adjusts the loss function model. Lastly, edge computing is deployed on the edge computing host to improve model efficiency for instant inference. This study inferred fifty-one 4096 × 4096 tagged UAV images taken in 2019 and calculated the confusion matrix and accuracy indices. The recall rate of the modified model in the normal rice category was increased by 13.7%. The affecting factor may be caused by changes in spatial resolution and differences in spectral values in different periods, which can be solved by adding part of the 2019 image transfer training to adjust the learning characteristics. The prior knowledge of a deep learning network can be deployed on edge computing devices to collect high-resolution images by regional routes planning within inferred disaster-damaged farmlands, providing efficient disaster survey tools with high detection accuracy. |
| 發表成果與AI計畫研究主題相關性 | 1.水稻災害監測與評估：在計畫主題之「智慧栽培」面向中，明確提到開發「倒伏辨識系統」，用於在水稻遭遇天災倒伏時，快速識別災損範圍並評估損失量 。 2.應用AI與無人機影像技術於農業：計畫強調導入人工智慧以提升農業栽培的韌性與永續發展，並在水稻栽培上進行落地驗證 。 3.技術方法改進：計畫的目標之一是達到節能、減碳、省工、永續的智慧新農業。本文章之研究成果為發展更有效率的災害評估工具，有助於實現省工的目標，並為後續的防災、減災策略提供技術支持，符合計畫的總體方向。 |
