本文選題：圖像分割　切入點(diǎn)：番茄葉片　出處：《農(nóng)業(yè)機(jī)械學(xué)報(bào)》2017年09期

【摘要】：在基于機(jī)器視覺的作物營(yíng)養(yǎng)診斷研究中,通常需要采集葉片樣本并在實(shí)驗(yàn)室條件下定量測(cè)定其營(yíng)養(yǎng)素含量,但由于葉片間相互重疊,往往使得葉片樣本不能清晰地反映在群體番茄冠層圖像中。為了解決這一問(wèn)題,需要利用圖像分析技術(shù)有效提取作物冠層圖像中的葉片,并根據(jù)處理結(jié)果采集實(shí)驗(yàn)室測(cè)定樣本。本文從復(fù)雜背景剔除、梯度圖計(jì)算、小波變換、標(biāo)記選取、分水嶺分割等環(huán)節(jié)出發(fā),實(shí)現(xiàn)了基于小波變換與分水嶺算法融合的番茄冠層多光譜圖像葉片分割。首先對(duì)比了4種復(fù)雜背景剔除算法,發(fā)現(xiàn)當(dāng)增強(qiáng)因子a=1.3時(shí),基于歸一化植被指數(shù)(Normalized difference vegetation index,NDVI)的閾值分割目標(biāo)提取準(zhǔn)確,適合各種光照條件,時(shí)空復(fù)雜度低。其次在梯度圖計(jì)算方面,近紅外(Near infrared,NIR)波段圖像形態(tài)學(xué)梯度在保持目標(biāo)邊緣的同時(shí),能消除大量由葉脈、光照等引起的葉片內(nèi)紋理細(xì)節(jié)。然后以小波分析為基礎(chǔ)進(jìn)行標(biāo)記選取,發(fā)現(xiàn)當(dāng)選取db4小波函數(shù)、4層小波分解低頻系數(shù)、閾值為18的H-maxima變換能得到最優(yōu)的目標(biāo)標(biāo)記結(jié)果。最后對(duì)多光譜番茄冠層圖像的小波變換分水嶺分割和數(shù)學(xué)形態(tài)學(xué)分水嶺分割結(jié)果進(jìn)行疊加,發(fā)現(xiàn)對(duì)復(fù)雜背景及不同光照強(qiáng)度下的番茄冠層葉片平均誤分率為21%,為基于多光譜圖像分析的番茄葉片營(yíng)養(yǎng)素含量檢測(cè)提供了一定的技術(shù)支持。
[Abstract]:Based on the research of crop nutrition diagnosis of machine vision, usually need to collect leaf samples and determine its nutrient content quantitatively under laboratory conditions, but because the blades overlap each other, often makes the leaf samples could not be clearly reflected in the group of tomato canopy image. In order to solve this problem, the need for effective extraction of leaf crop canopy image by using the technique of image analysis, and according to the results of laboratory samples. The determination of acquisition removed from the complex background, gradient map, wavelet transform, marker selection, and other aspects of a watershed segmentation, the segmentation of tomato canopy based on wavelet transform and watershed algorithm based on multi spectral image. Firstly, leaves compared to the 4 kinds of complex background excluding the algorithm, when the enhancement factor of a=1.3, based on the normalized difference vegetation index (Normalized difference vegetation index, NDVI) threshold segmentation Target extraction accuracy, suitable for all kinds of light conditions, time and space complexity is low. Secondly, in the calculation of the gradient map, near infrared (Near infrared, NIR) band image morphological gradient in the edge of the target at the same time, can be eliminated by a large amount of veins, light induced leaf texture details. Then based on wavelet analysis of mark the selected, found that when the DB4 wavelet function is selected, the 4 layer wavelet decomposition coefficients, the threshold for the 18 H-maxima transform can get the optimal target mark. At the end of the tomato canopy multispectral image wavelet transform and watershed segmentation and mathematical morphology watershed segmentation results are superimposed, tomato canopy on the complicated background and different illumination intensity the average error rate of 21%, provides technical support for the detection of tomato leaf nutrient analysis of multi spectral images based on content.

【作者單位】： 蘭州城市學(xué)院電子與信息工程學(xué)院;佛羅里達(dá)大學(xué)農(nóng)業(yè)與生物工程系;中國(guó)農(nóng)業(yè)大學(xué)現(xiàn)代精細(xì)農(nóng)業(yè)系統(tǒng)集成研究教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(31360291、31271619) 國(guó)家留學(xué)基金委西部地區(qū)人才培養(yǎng)特別項(xiàng)目(201408625069) 蘭州城市學(xué)院博士科研啟動(dòng)基金項(xiàng)目(LZCU-BS2013-07)
【分類號(hào)】：S641.2;TP391.41

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