我們提出一個以影像分析輔助自動化植物繁化過程的架構，在此架構中，首先要找出影像中側芽點(生長點)的位置和數量，進一步才能給出建議的分割線(下刀位置)，交由自動化手臂針對建議的分割線進行切割植物。對於嬌貴的高經濟作物而言， 作物而言，此自動化繁化系統可減少人工切割植物之人力花費與時間成本，且自動化的環境可減少植物因溫度變化或暴露於一般空氣中過久而壞死的可能性。所開發的影像分析演算法分成三個階段:植物分割(包含前處理)、特徵擷取和側芽點位置偵測。在植物分割階段主要仰賴k-mean分群演算法，針對像素顏色和位置進行分群，並根據群大小與特徵判斷是否需進行合併，最後找出植物主體區域。而前處理除了填補分割不完全產生的洞，也另外加入平滑邊界的機制以達到更佳的分割結果。利用取得的植物輪廓，我們利用取得的植物輪廓，我們計算每個輪廓點的曲率以及整個物體的骨架，產生多個不同的特徵描述子。最後我們使用兩種方法:(1)基於條件判斷基於條件判斷的直覺方法，(2)基於支持向量機基於支持向量機 (SVM )的機器學習方法，去判斷側芽點出現的位置。根據實驗結果，支持向量機的精確率(precision)和回應率(recall)都明顯優於條件判斷的直覺方法。

In this work, we propose an efficient plant propagation system with the aid of image analysis techniques. A lateral bud detection algorithm is designed and applied to determine the cutting line for plant propagation. We further develop an automatic control system to cut the plant based on the recommended cutting line, and the overall system aims at reducing labor cost and increasing the effectiveness of cutting the plants in the propagation process. Three steps including plant segmentation, feature extraction, and lateral bud positioning are designed to achieve fully automatic lateral bud detection. The plant segmentation step is based on K-means clustering, and we design a cluster merging mechanism and a smoothing process to obtain better segmentation results. Skeleton-based and curvature-based features are then extracted for the lateral bud positioning step. We investigate into two different approaches of lateral bud positioning, i.e. the heuristic thresholding approach and the SVM-based learning approach. The corresponding precision and recall rates show that the SVM-based approach performs better.

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