2025-05-13
【學術亮點-頂級期刊論文】結合機器學習與無人機多光譜影像的水稻氮肥準精監測
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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2025-05-12
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：Precision monitoring of rice nitrogen fertilizer levels based on machine learning and UAV multispectral imagery 中文：結合機器學習與無人機多光譜影像的水稻氮肥準精監測 |
| 期刊名稱 | Computers and Electronics in Agriculture (指標清單期刊) |
| 發表年份, 卷數, 起迄頁數 | 2025, 237(Part A), no.110523 |
| 作者 | Yang, Ming-Der (楊明德); Hsu, Yu-Chun*; Yi-Hsuan Chen; Chin-Ying Yang(楊靜瑩); Kai-Yun Li (瑞士學者) |
| DOI | 10.1016/j.compag.2025.110523 |
| 中文摘要 | 水稻是全球主要糧食作物，有效的氮肥管理對於優化產量同時最大限度地減少環境影響至關重要。本研究將無人機 (UAV) 影像與多光譜感測器和機器學習 (ML) 方法結合，對稻田中的氮肥等級分類。 在2020 年和 2021 年，設計不同氮肥含量，分為施肥不足、最佳施肥和施肥過量等級的實驗田。無人機搭載多光譜相機透過縝密分型任務拍攝，所有資料經過幾何和光譜校正，並利用決策樹分類器分割水稻與背景像素，召回率為 95.3%，整體準確率為 88.8%。研究中轉換 16 個光譜和表型體特徵，輸入到SVM和KNN模型中，並以特徵選擇方法來提高訓練成效。 模型測試成果中，SVM 模型的表現優於 KNN 模型，在第二期作。以卡方分析萃取特徵時，整體準確率達到 90.0%。RERVI和綠覆率是分類最重要的特徵。本研究中基於無人機的多光譜影像和機器學習的結合，提高氮肥分類的準確性和可擴展性。本研究提出之方法為精準農業和永續施肥管理提供一種量化監測模式。 |
| 英文摘要 | Rice is the primary food crop globally, and effective nitrogen fertilizer management is essential for optimizing yield while minimizing environmental impact. This study integrated unmanned aerial vehicle (UAV) imagery with multispectral imaging and machine learning (ML) methods to classify nitrogen levels (N levels) in rice fields. Experimental fields with various N levels (underfertilized, optimal fertilization, and overfertilized) were imaged in 2020 and 2021 by using UAVs. The captured images underwent geometric and spectral corrections, and rice pixel segmentation was performed using a decision tree classifier, which achieved a recall of 95.3 % and an overall accuracy of 88.8 %. N level classification was performed by extracting 16 spectral and structural features from the images, including color space transformations, vegetation indices, and canopy coverage. These features were input to support vector machine (SVM) and k nearest neighbors (KNN) models, and feature selection methods were applied to improve performance. The SVM model outperformed the KNN model, particularly in Period II, achieving an overall accuracy of 90.0 % when the chi-square feature selection method was applied. The Red Edge Ratio Vegetation Index and canopy coverage were the most informative features for classification. The integration of UAV-based multispectral imagery and ML in this study enhanced nitrogen classification accuracy and scalability. The method provides a data-driven approach for precision agriculture and sustainable fertilization management. |
| 發表成果與AI計畫研究主題相關性 | 利用AI機器學習方法分類無人機多光譜影像中的稻田氮含量等級，以利精準農業永續施肥管理。 |
