基于色飽和度三維幾何特征的馬鈴薯芽眼識別

李玉華，李天華，牛子孺，吳彥強，張智龍，侯加林,2

?

基于色飽和度三維幾何特征的馬鈴薯芽眼識別

李玉華1，李天華1，牛子孺1，吳彥強1，張智龍1，侯加林1,2※

（1. 山東農(nóng)業(yè)大學(xué)機械與電子工程學(xué)院，泰安 271018；2. 山東省園藝機械與裝備重點實驗室，泰安 271018）

芽眼準確識別是馬鈴薯種薯自動切塊的前提，為降低誤識別率，提高芽眼識別率和可靠性，提出基于色飽和度三維幾何特征的馬鈴薯芽眼識別方法。對分量在三維幾何空間進行分析提出了以其縱向截面曲線及其一階導(dǎo)數(shù)為基礎(chǔ)的4個特征向量，利用四特征綜合判定準則進行芽眼縱向識別，然后根據(jù)芽眼橫向特點對芽眼縱向識別結(jié)果進行再次篩選，降低芽眼誤識別率。試驗結(jié)果表明：該方法芽眼識別率達91.48%，其中，未發(fā)芽芽眼識別率92.21%，已發(fā)芽芽眼識別率89.00%，芽眼誤識別率4.32%，識別單幅圖像的平均耗時為2.68 s。因芽眼誤識別造成種薯切塊無芽眼的概率小于1.01%。試驗證明該方法抗干擾能力強，能有效降低誤識別率，防止種薯切塊無芽眼引起的缺苗現(xiàn)象，可為馬鈴薯種薯自動切塊機芽眼識別提供參考。

農(nóng)作物；圖像處理；圖像識別；馬鈴薯芽眼識別；色飽和度；三維幾何特征

0 引 言

馬鈴薯是中國第四大糧食作物，近幾年馬鈴薯種植面積逐年增加，馬鈴薯已經(jīng)成為重要的糧菜兼用作物[1-3]。為確保馬鈴薯早出苗、苗壯、提高產(chǎn)量及節(jié)省種薯，馬鈴薯播種前需根據(jù)農(nóng)藝要求對種薯進行切塊，切塊的芽眼位置、切塊質(zhì)量對馬鈴薯后期生長具有重要的影響[4-5]。由于種薯切塊要求高，當(dāng)前馬鈴薯切塊主要通過人工完成。人工種薯切塊勞動強度大、要求作業(yè)人員經(jīng)驗豐富、不同人員加工的切塊差異顯著且人工成本高。市場迫切需求一款自動化程度高、可靠高效的種薯切塊機器。但由于馬鈴薯芽眼識別困難且準確率低嚴重制約了種薯切塊機的發(fā)展。

目前，國內(nèi)外利用機器視覺技術(shù)對馬鈴薯檢測及分級進行了廣泛研究。如郝敏等[6]利用Zernike特征參數(shù)對馬鈴薯薯形檢測，并剔除畸形馬鈴薯。汪成龍等[7]利用流行學(xué)習(xí)算法對馬鈴薯機械損傷進行檢測?？讖埖萚8]通過機器視覺檢測馬鈴薯質(zhì)量和形狀。周竹等[9]通過高光譜成像技術(shù)[10-13]對常見馬鈴薯外部缺陷進行檢測。Noordam等[14]、Razmjooy等[15]利用機器視覺對馬鈴薯進行檢測及分級。上述研究能利用圖像識別方法對馬鈴薯進行有效檢測及分級。Moallem等[16]利用多層感知神經(jīng)網(wǎng)絡(luò)[17-19]對馬鈴薯圖像進行分割。田海韜等利用歐式距離及動態(tài)閾值分割進行芽眼區(qū)域識別[20]及標記[21-22]。邢作常等[23]采用形態(tài)學(xué)進行芽眼分割。張國強[24]提出采用邊緣檢測及形態(tài)學(xué)處理提取芽眼區(qū)域。Martínez等[25]利用模糊邏輯視覺系統(tǒng)對馬鈴薯幼苗的腋芽進行識別。上述研究能夠?qū)Ρ砻鏌o破皮等機械損傷的馬鈴薯芽眼準確的識別，但由于馬鈴薯生長在地下，其生長環(huán)境復(fù)雜并且挖掘時較易產(chǎn)生損傷，提高芽眼識別算法的抗干擾能力是實際生產(chǎn)中亟待解決的問題。

利用分類器[26-28]或深度學(xué)習(xí)[29-33]對物體局部特征識別已經(jīng)取得了良好的效果。但是馬鈴薯表面為不規(guī)則的橢球體且局部有較多凸起及凹陷，對光線反射不均勻，難以通過灰度圖像進行芽眼的識別；芽眼特征不明顯且易與斑點、泥土及破皮等機械損傷混淆。而與圖像亮度相關(guān)的圖像分割法則極易產(chǎn)生芽眼誤判。因此，借鑒馬鈴薯檢測及分級采用的相關(guān)機器視覺算法，尋找馬鈴薯芽眼識別的新算法具有重要的現(xiàn)實意義。

1 材料與方法

1.1 圖像的獲取裝置

圖像采集裝置如圖1所示，包括計算機、支架、4個環(huán)形補光燈、USB攝像頭和黑布組成。USB攝像頭的最大分辨率1 600í960像素，幀率30幀/s，USB3.0接口。計算機操作系統(tǒng)Windows 7，搭載Intel @ Core(TM) i5-6200U CPU @ CPU 2.30 GHz處理器，內(nèi)存8 GB，分析軟件為MATLAB R2016a。選用的環(huán)形補光燈功率25 W，以提高采集的圖像質(zhì)量，避免因自然光引起的干擾。

圖1 馬鈴薯圖像采集裝置

1.2 色彩空間選擇及圖像預(yù)處理

對常用的色彩空間對比分析發(fā)現(xiàn)，以物體表面對三色的反射量為基礎(chǔ)[34]的RGB色彩空間對芽眼特征識別較易受光照條件的影響，識別準確度低。HSV、NTSC、HSI色彩空間均以亮度、色調(diào)、色飽和度為分量描述色彩[35-38]，利用芽眼色調(diào)或色飽和度進行芽眼識別可以避免光照不均勻造成的干擾。其中，HSI色彩空間利用圓錐空間模型進行描述，能準確描述色調(diào)、亮度和色飽和度的變化。通過對各色彩空間不同分量進行對比分析，發(fā)現(xiàn)HSI色彩空間中的色飽和度分量中芽眼特征最明顯，且受光照條件影響較小，因此選用HSI色彩空間中的色飽和度分量為芽眼識別主要依據(jù)。

為消除噪音及背景區(qū)域?qū)ρ垦圩R別的干擾，提高識別準確度，對JPG圖像進行預(yù)處理，分析軟件MATLAB R2016a，其效果如圖2所示，方法如下：

1）通過MATLAB讀取圖像采集系統(tǒng)采集的JPG格式的圖像并轉(zhuǎn)換到RGB色彩空間，然后轉(zhuǎn)換到HSI色彩空間[39]并提取色飽和度分量，如圖2b所示。

2）以分量矩陣的行列數(shù)為和坐標，將色飽和度分量擴大255倍并以其為坐標轉(zhuǎn)化為三維圖示，如圖2c所示。

3）對原始RGB圖像進行二值化處理以區(qū)分背景區(qū)域和馬鈴薯區(qū)域，背景區(qū)域為1馬鈴薯區(qū)域為0，然后分量與該二值化矩陣點乘得到背景區(qū)域的分量，對原分量減去背景區(qū)域的分量得到馬鈴薯區(qū)域的分量。最后通過中值濾波降噪得到預(yù)處理后的色飽和度分量圖，如圖2d所示。

1.3 特征選擇

馬鈴薯芽眼呈狹長的凹陷，形狀類似人眼，凹陷由中心向外逐漸變淺，芽眼長邊與馬鈴薯長軸垂直，芽眼長邊呈弧線狀凸起。芽眼的這些特征極明顯，并且其結(jié)構(gòu)約束條件多，泥土殘留、病斑、污斑和機械損傷等偶然形成的輪廓與芽眼輪廓類似的概率極低。

色飽和度分量的三維圖能準確表達上述芽眼的獨有特征，如圖3所示，觀察發(fā)現(xiàn)芽眼區(qū)域具有顯著的特點。剛發(fā)芽的芽眼分量三維幾何圖的特征是：一側(cè)有一弧形的凹陷，緊接著是陡峭的山峰且中間為很深的圓形凹陷；短型芽眼分量三維幾何圖的特征是：下側(cè)有一弧形的凹陷，緊接著是陡峭的山峰，然后是相對平緩的高原狀；已發(fā)芽的芽眼分量三維幾何圖的特征是：下側(cè)有一弧形的凹陷，緊接著是多個陡峭的山峰；狹長芽眼分量三維幾何圖的特征與短型芽眼類似。非芽眼區(qū)域的分量三維幾何圖都較為平坦且分量值較小。

圖2 RGB原始圖像與S分量三維幾何圖

進一步通過對色飽和度分量三維圖進行縱向截取并分析找到合適的特征量，圖4分別為3個不同芽眼區(qū)域的典型截面曲線分析圖。很明顯在芽眼位置的曲線為波峰，且該波峰左側(cè)快速上升右側(cè)接近垂直下降。如圖5所示，分量截面曲線的一階導(dǎo)數(shù)在芽眼位置有一個明顯波谷，且波谷左側(cè)一定長度區(qū)間曲線與橫坐標形成的面積比右側(cè)一定長度區(qū)間曲線與橫坐標形成的面積大。

通過上述分析，選取分量截面曲線一階導(dǎo)數(shù)波谷對應(yīng)峰值的相對大?。ㄏ鄬φ麠l截面曲線的均值）、峰值前、后一定窗口內(nèi)的積分的比值及差值的大小和一階導(dǎo)數(shù)波谷前、后一定窗口內(nèi)的積分比值為芽眼特征參數(shù)。采用多特征參數(shù)進行篩選能有效防止因機械損傷等因素產(chǎn)生芽眼誤判，避免因芽眼誤判產(chǎn)生的種薯切塊無芽而導(dǎo)致缺苗現(xiàn)象。

圖3 S分量三維幾何圖及不同芽眼區(qū)域局部放大圖

注：箭頭標示芽眼位置。

1.4 芽眼識別算法

芽眼識別算法如下：

1）采集的圖像從RGB顏色空間轉(zhuǎn)化為HSI顏色空間并提取色飽和度分量。

2）對分量三維圖進行縱向（馬鈴薯的長軸方向）逐列截取分量曲線并求該曲線的一階導(dǎo)數(shù)。

3）尋找分量一階導(dǎo)數(shù)曲線的波谷位置，見圖5所示，波谷值PKS滿足式（1）。

式中PKS為第個波谷值；1為高度系數(shù)，該值為常數(shù)，通過對芽眼曲線統(tǒng)計得到，系數(shù)過大將造成芽眼漏識別，過小將引起誤識別；為分量導(dǎo)數(shù)。

4）對步驟3）的所有波谷根據(jù)式（2）～（5）求芽眼特征參數(shù)。

式中CP1、CP2、CP3、CP4為芽眼特征參數(shù)。1、2表示PKS對應(yīng)的前后窗口；max n表示PKS對應(yīng)的窗口1和2內(nèi)的最大值。

5）根據(jù)式（6）判定規(guī)則對芽眼進行識別，當(dāng)特征參數(shù)CP1、CP2同時大于響應(yīng)的閾值且當(dāng)特征參數(shù)CP3、CP4任意一個大于相應(yīng)的閾值時則相應(yīng)的行列置為0，否則置為1。

式中TH1、TH2、TH3和TH4為特征參數(shù)的閾值。

6）重復(fù)步驟2）～5）對分量三維圖所有縱向列進行芽眼特征判定，得到芽眼二值化矩陣。

注：PKSn為第n個波谷值；W1、W2為PKSn對應(yīng)的前后窗口；Smax n為PKSn對應(yīng)的窗口W1和W2內(nèi)的最大S值。

7）根據(jù)芽眼橫向連續(xù)性及芽眼下邊緣呈弧狀結(jié)構(gòu)的特點選擇合適的結(jié)構(gòu)元素，然后利用已選擇的結(jié)構(gòu)元素對芽眼二值化矩陣進行形態(tài)學(xué)處理。腐蝕操作可去掉二值化矩陣中寬度比芽眼的長度明顯小的區(qū)域，這些小的區(qū)域是偽芽眼引起的，然后利用膨脹操作恢復(fù)因腐蝕操作而縮小的芽眼區(qū)域。此步驟能進一步去掉偽芽眼，芽眼識別結(jié)束。

2 試驗及結(jié)果分析

2.1 試驗設(shè)計

試驗所用馬鈴薯為市場銷售的普通馬鈴薯，樣本數(shù)為100，每個馬鈴薯選2個面進行采集圖像，共采集圖像200幅，格式為JPG，分辨率1 600í960像素。整個測試過程操作系統(tǒng)Windows 7，搭載Intel @ Core(TM) i5-6200U CPU @ CPU 2.30 GHz處理器，內(nèi)存8 GB，分析軟件為MATLAB R2016a。

2.2 評價指標

本文將每幅圖像識別的芽眼數(shù)作為預(yù)測值，實際人工識別得到的芽眼數(shù)作為真實值。對馬鈴薯芽眼分為總芽眼、未發(fā)芽芽眼和已發(fā)芽芽眼3種情況，對3種情況分別通過計算所有圖像上算法成功識別的總個數(shù)與人工識別的真實個數(shù)做比值得到各情況的成功識別率，并計算因破皮、污斑、泥土等干擾造成的誤識別芽眼數(shù)占總芽眼數(shù)的百分比。

2.3 結(jié)果與分析

2.3.1 識別結(jié)果分析

芽眼識別系統(tǒng)主要有3個模塊組成，分別為色飽和度分量提取及預(yù)處理、芽眼三維幾何特征提取和芽眼形態(tài)學(xué)處理。各處理效果流程如圖6所示。

圖6 馬鈴薯芽眼識別流程

將特征參數(shù)的閾值TH1、TH2、TH3和TH4分別設(shè)置為3、2.8、1.5和15，對200幅馬鈴薯圖像進行了芽眼識別，識別結(jié)果如表1所示。共880個芽眼，成功識別出805個，成功率91.48%，芽眼漏識別率8.52%。其中，未發(fā)芽芽眼680，成功識別出627個，成功率92.21%。已發(fā)芽芽眼200，成功識別出178個，成功率89.00%。因破皮、污斑、泥土等干擾造成的誤識別芽眼38，占總芽眼數(shù)的4.32%，識別單幅圖像的平均耗時為2.68 s。

馬鈴薯種薯切塊需要滿足質(zhì)量在30～60 g范圍內(nèi)并且芽眼數(shù)至少1個。根據(jù)試驗分析及傳統(tǒng)經(jīng)驗知每個種薯塊大于1個芽眼的概率大于80%。因此，由式（7）知，因誤識別的芽眼而造成種薯切塊無芽眼的概率p小于1.01%。該方法對種薯芽眼識別的準確率完全能滿足種薯自動切塊機要求。

2.3.2 識別結(jié)果可靠性分析

馬鈴薯形狀不規(guī)則，且表面隨機性出現(xiàn)破皮、色斑、蟲眼、泥土和機械損傷等現(xiàn)象，這些現(xiàn)象對芽眼識別將造成較強的干擾，容易引起芽眼誤判。芽眼誤判將產(chǎn)生馬鈴薯種薯切塊無芽眼，從而在播種后產(chǎn)生缺苗現(xiàn)象并對產(chǎn)量造成嚴重的影響。芽眼漏識別將引起切塊數(shù)量減少及切塊重量的增加，但對產(chǎn)量沒有影響，芽眼漏識別的危害性相對較低。

表1 馬鈴薯種薯芽眼識別結(jié)果

通過色飽和度多個幾何特征參數(shù)進行芽眼識別能有效降低芽眼誤判率，提高識別結(jié)果的可靠性。試驗結(jié)果表明因干擾造成的芽眼誤識別率僅為4.32%。圖7為本文基于色飽和度三維幾何特征的馬鈴薯芽眼識別算法對干擾因素的識別情況，能有效防止芽眼的誤判。

圖7 表面缺陷對芽眼識別的影響

2.4 三維狀態(tài)馬鈴薯芽眼識別的可能性的討論

本文算法是針對二維圖像給出的，對于三維狀態(tài)的馬鈴薯芽眼識別需對不同角度的多張圖像進行識別。可以通過鏡面[14]或旋轉(zhuǎn)臺[40]對馬鈴薯進行多角圖像的采集，分別進行分析得到馬鈴薯各面的芽眼位置。以此作為準確識別芽眼的算法，然后利用馬鈴薯的輪廓快速建立近似的三維模型，通過兩者的信息融合滿足精度和速度的要求，為馬鈴薯種薯切塊機提供算法支持。

3 結(jié) 論

1）本文對飽和度分量的三維幾何特征、縱向截面曲線及其一階導(dǎo)數(shù)曲線進行分析提出了與芽眼相關(guān)的4個特征參數(shù)。通過求取各縱向截面4個特征參數(shù)并根據(jù)判定規(guī)則進行縱向芽眼識別，然后根據(jù)芽眼橫向特點利用數(shù)學(xué)形態(tài)學(xué)進行再次芽眼篩選。通過對芽眼多特征篩選，芽眼識別率91.48%。其中，未發(fā)芽芽眼識別率92.21%，已發(fā)芽芽眼識別率89.00%，芽眼誤識別率4.32%。

2）采用基于色飽和度三維幾何特征進行馬鈴薯芽眼識別，能有效降低因破皮、色斑、蟲眼、泥土和機械損傷等現(xiàn)象造成的干擾，本方法防干擾能力強，可靠性高，誤識別率為4.32%，計算表明：因誤識別芽眼而造成種薯切塊無芽眼的概率小于1.01%，防止因馬鈴薯切塊無芽眼缺苗造成減產(chǎn)。

[1] 屈冬玉，謝開云，金黎平，等. 中國馬鈴薯產(chǎn)業(yè)與現(xiàn)代農(nóng)業(yè)[J]. 農(nóng)業(yè)技術(shù)與裝備，2007(7)：4－7.

[2] 陳萌山，王小虎. 中國馬鈴薯主食產(chǎn)業(yè)化發(fā)展與展望[J]. 農(nóng)業(yè)經(jīng)濟問題，2015(12)：4－11.

[3] 楊帥，閔凡祥，高云飛，等. 新世紀中國馬鈴薯產(chǎn)業(yè)發(fā)展現(xiàn)狀及存在問題[J]. 中國馬鈴薯，2014，28(5)：311－316.

Yang Shuai, Min Fanxiang, Gao Yunfei, et al. Status quo and challenges of China potato industry of the 21st century[J]. Chinese Potato Journal, 2014, 28(5): 311－316. (in Chinese with English abstract)

[4] 樊祖立，牛力立，唐興發(fā)，等. 種薯切塊芽位及不同催芽、拌種方式對馬鈴薯農(nóng)藝性狀的影響[J]. 耕作與栽培，2017(5)：17－20.

Fan Zuli, Niu Lili, Tang Xingfa, et al. The effects of seed-cutting buds and different buds on potato agronomic traits[J]. Tillage & Cultivation, 2017(5): 17－20. (in Chinese with English abstract)

[5] 雷雪萍，李勇，白雅梅，等. 相同種薯播量不同種薯切塊重量和密度組合對植株和塊莖性狀的影響[J]. 中國馬鈴薯，2016，30(6)：336－340.

Lei Xueping, Li Yong, Bai Yamei, et al. Effects of various combinations of seed piece weight and in-row spacing on plant and tuber traits planting using equal quantity of seed[J]. Chinese Potato Journal, 2016, 30(6): 336－340. (in Chinese with English abstract)

[6] 郝敏，麻碩士，郝小冬. 基于Zernike矩的馬鈴薯薯形檢測[J]. 農(nóng)業(yè)工程學(xué)報，2010，26(2)：347－350.

Hao Min, Ma Shuoshi, Hao Xiaodong. Potato shape detection based on Zernike moments[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(2): 347－350. (in Chinese with English abstract)

[7] 汪成龍，李小昱，武振中，等. 基于流形學(xué)習(xí)算法的馬鈴薯機械損傷機器視覺檢測方法[J]. 農(nóng)業(yè)工程學(xué)報，2014，30(1)：245－252.

Wang Chenglong, Li Xiaoyu, Wu Zhenzhong, et al. Machine vision detecting potato mechanical damage based on manifold learning algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(1): 245－252. (in Chinese with English abstract)

[8] 孔彥龍，高曉陽，李紅玲，等. 基于機器視覺的馬鈴薯質(zhì)量和形狀分選方法[J]. 農(nóng)業(yè)工程學(xué)報，2012，28(17)：143－148.

Kong Yanlong, Gao Xiaoyang, Li Hongling, et al. Potato grading method of mass and shapes based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(17): 143－148. (in Chinese with English abstract)

[9] 周竹，黃懿，李小昱，等. 基于機器視覺的馬鈴薯自動分級方法[J]. 農(nóng)業(yè)工程學(xué)報，2012，28(7)：178－183.

Zhou Zhu, Huang Yi, Li Xiaoyu, et al. Automatic detecting and grading method of potatoes based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(7): 178－183. (in Chinese with English abstract)

[10] Camps-Valls G, Gomez-Chova L, Munoz-Mari J, et al. Composite kernels for hyperspectral image classification[J]. IEEE Geoscience & Remote Sensing Letters, 2006, 3(1): 93－97.

[11] Gowen A A, O'Donnell C P, Cullen P J, et al. Hyperspectral imaging an emerging process analytical tool for food quality and safety control[J]. Trends in Food Science & Technology, 2007, 18(12): 590－598.

[12] Zhao W, Du S. Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(8): 4544－4554.

[13] 鄭志雄，齊龍，馬旭，等. 基于高光譜成像技術(shù)的水稻葉瘟病病害程度分級方法[J]. 農(nóng)業(yè)工程學(xué)報，2013，29(19)：138－144.

Zheng Zhixiong, Qi Long, Ma Xu, et al. Grading method of rice leaf blast using hyperspectral imaging technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2013, 29(19): 138－144. (in Chinese with English abstract)

[14] Noordam J C, Otten G W, Timmermans T J M, et al. High-speed potato grading and quality inspection based on a color vision system[J]. Proceedings of SPIE The International Society for Optical Engineering, 2000, 3966: 206－217.

[15] Razmjooy N, Mousavi B S, Soleymani F. A real-time mathematical computer method for potato inspection using machine vision[J]. Computers & Mathematics with Applications, 2012, 63(1): 268－279.

[16] Moallem P, Razmjooy N. A multi layer perceptron neural network trained by invasive weed optimization for potato color image segmentation[J]. Trends in Applied Sciences Research, 2012, 6(6): 445－455.

[17] Mirjalili S. How effective is the grey wolf optimizer in training multi-layer perceptrons[J]. Applied Intelligence, 2015, 43(1):150－161.

[18] Amakdouf H, Mallahi M E, Zouhri A, et al. Classification and recognition of 3D image of charlier moments using a multilayer perceptron architecture[J]. Procedia Computer Science, 2018, 127: 226－235.

[19] Zhao Y, Zan Y, Wang X, et al. Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation[C]// IEEE Control and Decision Conference, Xuzhou, 2010: 3423－3427.

[20] 田海韜，趙軍，蒲富鵬. 馬鈴薯芽眼圖像的分割與定位方法[J]. 浙江農(nóng)業(yè)學(xué)報，2016，28(11)：1947－1953. Tian Haitao, Zhao Jun, Pu Fupeng. A method for recognizing potato’s bud eye[J]. Acta Agriculturae Zhejiangensis, 2016, 28(11): 1947－1953. (in Chinese with English abstract)

[21] 王見，周勤，尹愛軍. 改進Otsu算法與ELM融合的自然場景棉桃自適應(yīng)分割方法[J]. 農(nóng)業(yè)工程學(xué)報，2018，34(14)：173－180.

Wang Jian, Zhou Qin, Yin Aijun. Self-adaptive segmentation method of cotton in natural scene by combining improved Otsu with ELM algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(14): 173－180. (in Chinese with English abstract)

[22] Otsu N A. Threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 2007, 9(1): 62－66.

[23] 邢作常，田素博，劉思瑤，等. 基于機器視覺的種薯自動切塊機設(shè)計[J]. 農(nóng)機化研究，2016，38(10)：69－73.

Xing Zuochang, Tian Subo, Liu Siyao, et al. Design of automatic cutter for potato used as seeds based on machine vision[J]. Journal of Agricultural Mechanization Research, 2016, 38(10): 69－73. (in Chinese with English abstract)

[24] 張國強. 馬鈴薯種薯自動切塊機的研究與設(shè)計[D]. 呼和浩特：內(nèi)蒙古大學(xué)，2015.

Zhang Guoqiang. The Study and Design of the Potato Seed Auto Cutting Machine[D]. Hohhot: Inner Mongolia University, 2015. (in Chinese with English abstract)

[25] Martínez C L, Martínezrubin E, Floresgarcía F, et al. Identification of axillary buds of potato seedlings based on a vision system with fuzzy logic[J]. Phyton, 2011, 80(1): 79－84.

[26] Brabanter K D, Karsmakers P, Brabanter J D, et al. Confidence bands for least squares support vector machine classifiers: A regression approach[J]. Pattern Recognition, 2012, 45(6): 2280－2287.

[27] Huang H Z, Wang H K, Li Y F, et al. Support vector machine based estimation of remaining useful life: Current research status and future trends[J]. Journal of Mechanical Science & Technology, 2015, 29(1): 151－163.

[28] Huang X, Shi L, Suykens J A K. Asymmetric least squares support vector machine classifiers[J]. Computational Statistics & Data Analysis, 2014, 70(2): 395－405.

[29] Shah M P, Singha S, Awate S P. Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network[C/OL]// IEEE International Conference on Image Processing. Beijing, 2018[2018-09-07]. https://ieeexplore.ieee.org/document/8296403.DOI:10.1109/ICIP.2017.8296403.

[30] 黃雙萍，孫超，齊龍，等. 基于深度卷積神經(jīng)網(wǎng)絡(luò)的水稻穗瘟病檢測方法[J]. 農(nóng)業(yè)工程學(xué)報，2017，33(20)：169－176.

Huang Shuangping, Sun Chao, Qi Long, et al. Rice panicle blast identification method based on deep convolution neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(20): 169－176. (in Chinese with English abstract)

[31] Babenko A, Slesarev A, Chigorin A, et al. Neural codes for image retrieval[C]// European Conference on Computer Vision. Springer, 2014: 584－599.

[32] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]// Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016: 770－778.

[33] Bae W, Yoo J, Ye J C. Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification[C]//Computer Vision and Pattern Recognition Workshops. IEEE, 2017: 1141－1149.

[34] 李俊峰. 基于RGB色彩空間自然場景統(tǒng)計的無參考圖像質(zhì)量評價[J]. 自動化學(xué)報，2015，41(9)：1601－1615.

Li Junfeng. No-reference image quality assessment based on natural scene statistics in RGB color space[J]. Acta Automatica Sinica, 2015, 41(9): 1601－1615. (in Chinese with English abstract)

[35] 徐德政，唐新明，祝小勇，等. 基于HSI色彩空間的資源三號影像陰影檢測[J]. 測繪與空間地理信息，2017，40(1)：56－58.

Xu Dezheng, Tang Xinming, Zhu Xiaoyong, et al. The shadow detection algorithm based on HSI color space for ZY-3 satellite[J]. Geomatics & Spatial Information Technology, 2017, 40(1): 56－58. (in Chinese with English abstract)

[36] Ma J, Fan X, Yang S X, et al. Contrast limited adaptive histogram equalization-based fusion in YIQ and HSI color spaces for underwater image enhancement[J/OL]. International Journal of Pattern Recognition & Artificial Intelligence, 2018, 32(7): 1[2018-09-07]. https://www. worldscientific.com/doi/pdf/10.1142/S0218001418540198.

[37] 劉黎明. CLAHE算法在不同彩色空間中的圖像增強效果評估[J]. 艦船電子對抗，2016，39(6)：62－68.

Liu Liming. Image enhancement effect evaluation of CLAHE algorithm in different color spaces[J]. Shipboard Electronic Countermeasure, 2016, 39(6): 62－68. (in Chinese with English abstract)

[38] Gong L H, He X T, Tan R C, et al. Single channel quantum color image encryption algorithm based on HSI model and quantum fourier transform[J]. International Journal of Theoretical Physics, 2017, 57(1/2): 1－15.

[39] Wu F, Kintak U. Low-Light image enhancement algorithm based on HSI color space[C/OL]// IEEE International Congress on Image & Signal Processing. Shanghai, 2018: 1[2018-09-07]. https://ieeexplore.ieee.org/document/8301957. DOI:10.1109/CISP-BMEI.2017.8301957.

[40] 王傳宇，郭新宇，吳升，等. 基于計算機視覺的玉米果穗三維重建方法[J]. 農(nóng)業(yè)機械學(xué)報，2014，45(9)：274－279.

Wang Chuanyu, Guo Xinyu, Wu Sheng, et al. Three dimensional reconstruction of maize ear based on computer vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(9): 274－279. (in Chinese with English abstract)

Potato bud eyes recognition based on three-dimensional geometric features of color saturation

Li Yuhua1, Li Tianhua1, Niu Ziru1, Wu Yanqiang1, Zhang Zhilong1, Hou Jialin1,2※

(1.2710182.271018)

Potato is an important food crop with planting area been increased annually. Potato should be cut according to agronomic requirements before planting to make the potato sprout early and to increase the yield and to save the seed potato. The bud position and the weight of the seed potato cutting have an important influence on late growth of potato. Accurate identification of the bud eye is the premise of automatic cutting of seed potatoes. In addition, bud eyes are easy to be confused with mechanical damages such as growth spots, soil and skin breakage. In order to reduce the false recognition rate and improve the success rate of bud eyes recognition, a method for identifying potato buds based on three-dimensional geometric features of color saturation are proposed. By comparing the color space of potato, it is found that the color saturation componentin the HSI color space is the most obvious. In addition, it is less affected by light conditions, avoiding low recognition accuracy caused by interference of brightness factors. In this paper, four eigenvectors based on their longitudinal section curves and their first derivatives were proposed by analyzing thecomponent in the three-dimensional geometric space. The four-feature comprehensive judgment criterion was used to identify the bud eye longitudinally, and then the bud eye vertical recognition result was screened again according to the lateral characteristics of the bud eye. The modified method can greatly reduce the bud eye false recognition rate. The specific potato bud eyes recognition algorithm was as follows: 1) Color saturationcomponent was obtained by converting the acquired image from RGB color space to HSI color space. 2) Thecomponent curve was obtained by longitudinally column-by-column interception of the three-dimensional map of thecomponent. Then derivative curve was obtained by deriving thecomponent curve. 3) First, all the valley positions of the first derivative curve of thecomponent were obtained, and the four bud eye feature parameters corresponding to the position were obtained. Then, the bud eye was identified according to the determination rule. If the condition was satisfied, the corresponding row and column position was set to 0, otherwise it was set to 1. The bud eye binarization matrix was obtained by performing bud eye feature determination on all longitudinal columns of thecomponent three-dimensional map.4) According to the lateral continuity of the bud eye and the arc-like structure of the lower edge of the bud, the morphological processing of the bud eye binarization matrix was carried out, and the false bud eyes were further removed to complete the bud eyes recognition. The influence of defects such as broken skin, growth spots and mechanical damage on the accuracy of bud-eye recognition was also analyzed in this paper.The data indicated that mechanical damage, growth spots, etc. was not confused with bud eyes and caused misjudgment of the buds. The algorithm can effectively prevent the influence of these interference factors.The experimental results showed that the recognition rate of bud eyes was 91.48%, of which, the recognition rate of non-germinated buds, germinated buds and the false recognition rate of bud eyes was 92.21%, 89.00% and 4.32%, respectively. The probability of seed potato cutting without buds due to false recognition of bud eyes was less than 1.01%. This can effectively prevent the seedling shortage caused by potato cutting without buds, resulting in reduced production. The average time taken to identify a single image is 2.68 s. The results indicated that the method can provide reference for the bud eyes recognition of seed potato automatic cutting machine due to its low false recognition rate of bud eye, strong anti-interference ability and high stability.

crops; image processing; image recognition; potato bud eyes recognition; color saturation; 3D geometric features

李玉華，李天華，牛子孺，吳彥強，張智龍，侯加林. 基于色飽和度三維幾何特征的馬鈴薯芽眼識別[J]. 農(nóng)業(yè)工程學(xué)報，2018，34(24)：158－164. doi：10.11975/j.issn.1002-6819.2018.24.019 http://www.tcsae.org

Li Yuhua, Li Tianhua, Niu Ziru, Wu Yanqiang, Zhang Zhilong, Hou Jialin. Potato bud eyes recognition based on three-dimensional geometric features of color saturation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(24): 158－164. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2018.24.019 http://www.tcsae.org

2018-09-07

2018-11-22

十三五國家重點研發(fā)計劃智能農(nóng)機裝備專項“馬鈴薯精量播種技術(shù)與裝備研發(fā)”（2017YFD0700705）

李玉華，講師，博士研究生，主要從事智能農(nóng)業(yè)裝備研究。Email：liyuhua@sdau.edu.cn

侯加林，教授，博士生導(dǎo)師，主要從事智能農(nóng)業(yè)裝備研究。Email：jlhou@sdau.edu.cn

10.11975/j.issn.1002-6819.2018.24.019

TP391.41

A

1002-6819(2018)-24-0158-07