2025-03-14
【學術亮點-頂級期刊論文】利用無人機多光譜影像和機器學習對穀粒含水量之精準評估
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【學術亮點-頂級期刊論文】利用無人機多光譜影像和機器學習對穀粒含水量之精準評估
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2025-03-01
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】
| 論文篇名 | 英文：Precision assessment of rice grain moisture content using UAV multispectral imagery and machine learning 中文：利用無人機多光譜影像和機器學習對穀粒含水量之精準評估 |
| 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, 230, no.109813 |
| 作者 | Yang, Ming-Der (楊明德); Hsu, Yu-Chun*; Tseng, Wei-Cheng; Tseng, Hsin-Hung; Lai, Ming-Hsin |
| DOI | 10.1016/j.compag.2024.109813 |
| 中文摘要 | 氣候變遷影響下，判斷最佳水稻收穫時間變得越來越複雜。傳統方法基於農民經驗、勞力密集、或破壞性水分計來評估穀粒水分含量 (GMC) 決定收穫時間，GMC則會影響稻米收購價格。本研究利用無人機 (UAV) 收集多光譜影像進行 GMC 估算，整合特徵轉換（FC）、特徵選擇（FS）和機器學習（ML），開發一非破壞性GMC定量估計模型，並在實驗和實際田地中的五個作物季節進行測試。地面真實數據，GMC範圍從 19.71% 到 43.82%，強調模型廣泛的適用性。優化後的FC+FS+ML流程顯著改善模型成效，平均絕對誤差（MAE）降至1.15％，低於傳統方法（1.74％），凸顯 FC 和 FS 在最小化冗餘特徵的有效性。此模式應用於規模化水稻收成，可大幅減少八倍勞力和時間。本研究製作GMC分布圖分析GMC的空間分佈，幫助農民優化收穫計畫，從而提高利潤和作物品質，以確保農民的穩定收入和保障糧食供應，並減輕極端天氣的風險。 |
| 英文摘要 | Amidst climate change, determining optimal rice harvest timing is increasingly complex. Traditional and common methods, based on farmers’ experience, impact rice’s purchase price and rely on labor-intensive, destructive moisture meters for Grain Moisture Content (GMC) assessment. Addressing these challenges, this study utilizes an Unmanned Aerial Vehicle (UAV) to collect multispectral imagery for GMC estimation. This research integrated Feature Conversion (FC), Feature Selection (FS), and Machine Learning (ML) to develop a non-destructive GMC quantitative estimation model, tested across five crop seasons in both experimental and practical fields. The ground truth data, ranging from 19.71 % to 43.82 % GMC, underscore its extensive applicability. The optimized FC + FS + ML procedure significantly improves, with the Mean Absolute Error (MAE) reduced to 1.15 %, lower than traditional methods (1.74 %). This reduction highlights the effectiveness of FC and FS in minimizing redundant features. Applied in large-scale farming, the model substantially reduces labor and time by eightfold. The paddy-based GMC mapping in this research evaluates GMC spatial distribution, assisting farmers in optimizing harvest schedules, and thereby enhancing both profit and crop quality. This approach promises stable income for farmers and a secure food supply, mitigating risks associated with extreme weather. |
| 發表成果與AI計畫研究主題相關性 | 利用機器學習方法辨識無人機照片中穀粒含水量，以製作GMC分布圖分析水稻GMC的空間分佈。 |
