多時期無人機影像於稻作植株高度量測之應用

稻作為世界重要糧食，尤其在亞洲地區。稻作高度是一重要生長性狀，施肥過多使植株生長過高易導致倒伏，施肥過少植株過矮則影響稻作的產量。實務上農務決策皆以經驗判斷，傳統學術調查使用木尺量測稻作高度。隨著無人飛行載具（Unmanned Aerial Vehicle, UAV）的普及與技術的進步，專家得以使用遙測方法大面積調查農藝性狀。本研究為了使用UAV監測各肥分等級之稻作在各生長階段的高度變化，除了制定UAV稻作監測飛行條件外，亦比較K-means分群法（K-means Clustering, or Lloyd-Forgy algorithm）與百高平均法獲取各樣區稻作代表高度，並追蹤各生長階段的植株高度變化，以供耕作決策參考。藉由空拍影像分析判釋調查水稻植株高度之變化，未來可建立智慧化生產與風險管理預測，擬定各生育期建議之栽培管理與應對方針。

關鍵字

無人機；數值地表模型；精準農業； K-means分群法

並列摘要

Rice is one of the major crops in the world, especially in Asia. The height of rice is an important feature of growth health. Over-fertilization makes plants grow too high so to tend to rice lodging, while less-fertilization makes plants too short so to yield incompletely. In practice, panicle fertilization decision making is judged by experience. Traditionally academic survey of rice height uses wooden rulers to measure the rice height in the field. With the popularization of Unmanned Aerial Vehicles (UAV) and the advancement of technology, experts have been able to use remote sensing methods to investigate agricultural heterogeneous traits on a large scale. This study uses UAV to measure the height variation of rice for various levels of fertilization at different growth stages. This study also formulating UAV flight procedure of rice monitoring. Top 100 height average and K-means clustering (or Lloyd-Forgy algorithm) were executed and compared for the measurement of rice height in a paddy. The variation of rice height can be monitored as a reference for cultivation decision, smart yield, and risk management.

並列關鍵字

Unmanned Aerial Vehicle ； Digital Surface Model ； Precision Agriculture ； K-means Clustering

國際替代計量

多時期無人機影像於稻作植株高度量測之應用

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