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基于圖像處理與支持向量機的樹上蘋果早期估產(chǎn)研究
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國家自然科學基金資助項目(31371537)、保定市科學研究與發(fā)展計劃資助項目(14ZN029)和河北省科技計劃資助項目(13227431)


Early Yield Estimation of ‘Gala’ Apple Trees Using Image Processing Combined with Support Vector Machine
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    摘要:

    為了實現(xiàn)果樹掛果期的精準管理,,針對樹上蘋果早期估產(chǎn)問題,,以“Gala”蘋果為研究對象,,開展了對定果后的果樹進行產(chǎn)量估測的研究,,提出了一種圖像處理結合支持向量機的樹上蘋果早期估產(chǎn)方法,。首先在蘋果園內獲取定果后的果樹樹冠圖像,,此時的樹上蘋果顏色為綠色(本文稱此時期為果樹青果期),;采用分析圖像各顏色分量值分布圖法,確定在YCbCr顏色空間中,,以Cb≤100與Cr≥120作為分割樹冠圖像中蘋果的條件,;從果樹樹冠圖像中提取果實個數(shù),、果實面積,、果實樹葉比、受遮擋果實個數(shù)比例及受遮擋果實面積比例,;以上述5個特征參數(shù)作為輸入,,實際產(chǎn)量為輸出,利用支持向量機方法建立樹上蘋果早期估產(chǎn)模型,。本文利用訓練集(含50個樣本)訓練模型,,預測產(chǎn)量與實際產(chǎn)量的決定系數(shù)R2達到了0.7242,均方根誤差RMSE為1.71kg,,平均絕對百分比誤差MAPE為9%,,平均預測誤差MFE為0.21。利用測試集(含15個樣本)測試模型,,得到RMSE為2.45kg,,MAPE為13%,。結果表明該模型不僅具有較好的預測精度與無偏性,且具有較好的魯棒性,,所采用的樹上蘋果早期估產(chǎn)方法可行,。

    Abstract:

    Early fruit-yield forecasting plays an important role in productive and sustainable management of apple orchards. This paper presents a method combining image processing with support vector machine (SVM) technology to build a prediction model for early season apple tree yield estimation. Sixty ‘Gala’ apple trees were randomly selected for study. Initially, tree canopy images were captured in natural light just after June drop when the fruit color was green. Apples in the canopy image were identified with the condition Cb≤100 and Cr≥120 obtained by analyzing the distribution map of color component values in YCbCr color space, in which Y was the luminance component, Cb and Cr were the blue-difference and red-difference chroma components. By the same method, the condition Cr≤125 was used to segment foliage from canopy image with fruit removed. Five characteristics were extracted from the canopy image: fruit total area, total number of fruit, proportion of fruit total area to foliage area, proportion of total fruit area shaded by leaves to total fruit area, and proportion of total fruit numbers shaded by leaves to total fruit number. Finally, the SVM method was employed to build a yield estimation model with these five characteristics as input parameters and the actual yield as output. A randomized sample set containing 50 trees was used to train the model, yielding a coefficient of determination (R2) of 0.7242, a root mean square error (RMSE) of 1.71kg, a mean absolute percentage error (MAPE) of 9% and an average prediction error (MFE) of 0.21. Using 15 independent samples, the model was validated, yielding a RMSE of 2.45kg and a MAPE of 13%. The proposed model showed significant potential for early apple yield prediction of individual trees with potential application to other fruit crops.

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程 洪,Lutz Damerow, Michael Blanke,孫宇瑞.基于圖像處理與支持向量機的樹上蘋果早期估產(chǎn)研究[J].農(nóng)業(yè)機械學報,2015,46(3):9-14,22. Cheng Hong, Lutz Damerow, Michael Blanke, Sun Yurui. Early Yield Estimation of ‘Gala’ Apple Trees Using Image Processing Combined with Support Vector Machine[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(3):9-14,22.

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  • 收稿日期:2014-05-11
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  • 在線發(fā)布日期: 2015-03-10
  • 出版日期: 2015-03-10
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