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基于高光譜圖像與果蠅優(yōu)化算法的馬鈴薯輕微碰傷檢測
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國家自然科學基金項目(61275156)和湖北省自然科學基金重點項目(2011CDA033)


Detection of Potato Slight Bruise Based on Hyperspectral Image and Fruit Fly Optimization Algorithm
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    摘要:

    針對通常采用的反射高光譜無法準確檢測隨機放置馬鈴薯表面輕微碰傷的問題,,提出了一種用V型平面鏡的高光譜并結合果蠅優(yōu)化算法(FOA)檢測馬鈴薯輕微碰傷的方法,。試驗搭建了V型平面鏡反射高光譜圖像采集系統(tǒng),,分別采集隨機放置下的輕微碰傷和合格馬鈴薯的高光譜圖像,,每張高光譜圖像包含平面鏡1反射圖像F1,、相機直接采集圖像F2,、平面鏡2反射圖像F3,,分別提取F1,、F2,、F3感興趣區(qū)域的平均光譜拼接成馬鈴薯的屬性矩陣。采用標準正態(tài)變量變換(SNV)預處理后的光譜矩陣進行全波段的支持向量分類機(SVC)建模,,預測集的識別率僅為84.11%,;為了提高模型的性能,采用蟻群算法(ACO)進行變量優(yōu)選,,優(yōu)選出9個變量建立的SVC模型預測準確率為95.32%,;分別用網格搜索法(Grid search)、遺傳算法(GA)和FOA對SVC的懲罰參數c和核函數參數g進行尋優(yōu),,通過比較分析,,FOA-SVC對訓練集和預測集的識別準確率均達到100%。試驗結果表明,,用V型平面鏡的高光譜結合FOA-SVC能夠準確檢測馬鈴薯的輕微碰傷,,可為馬鈴薯的輕微碰傷在線檢測提供技術基礎。

    Abstract:

    Potato is an indispensable food crop for the people in the world. As a kind of light injury on the surface of potato,,slight bruise of potato cannot be accurately tested when potato placed in random orientation. This paper proposed a method by combining hyperspectral image based on Vshaped plane mirror with fruit fly optimization algorithm (FOA) to identify slight bruise of potato randomly placed. In this study, hyperspectral imaging system was built based on Vshaped plane mirror and 322 potato samples were bought as the research subjects. To meet with the practical production, within half an hour after bruise occurred, potatoes were placed in three positions: the damage part facing to camera, side to camera, and back to camera. Then hyperspectral images of all potatoes were collected including reflection image F1 in mirror 1, image F2 directly obtained by camera and reflection image F3 in mirror 2. Average spectrums from these three images were spliced into attribute matrix of sample. Support vector classifier(SVC) model was established in full bands after utilizing standard normal variate(SNV) and the recognition accuracy of prediction set was only 8411%. Variable selection was processed by ant colony optimization(ACO). Nine spectral variables (762nm, 879nm in F1; 711nm, 957nm, 1020nm in F2; 510nm, 746nm, 1.000nm, 1.007nm in F3 )were selected and the recognition rate reached 95.32%. FOA, genetic algorithm(GA) and grid search were respectively applied to search the best penalty parameter c and kernel function parameter g. By comparing results of those models, FOA obtained optimal parameters(c=11.0763,,g=9.2625). FOA-SVC was proved to be the best model and the training set and prediction set recognition accuracy both reached 100%. The results show that the combination of hyperspectral image based on Vshaped plane mirror with FOA-SVC could accurately detect the slight bruise of potato.

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李小昱,徐森淼,馮耀澤,黃濤,丁崇毅.基于高光譜圖像與果蠅優(yōu)化算法的馬鈴薯輕微碰傷檢測[J].農業(yè)機械學報,2016,47(1):221-226. Li Xiaoyu, Xu Senmiao, Feng Yaoze, Huang Tao, Ding Chongyi. Detection of Potato Slight Bruise Based on Hyperspectral Image and Fruit Fly Optimization Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):221-226.

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  • 收稿日期:2015-06-24
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  • 在線發(fā)布日期: 2016-01-10
  • 出版日期: 2016-01-10
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