本文關鍵詞：基于圖像分析技術的小麥群體農學參數(shù)獲取與群體質量評價研究　出處：《揚州大學》2016年博士論文　論文類型：學位論文

  更多相關文章： 小麥 圖像分析 農學參數(shù) 群體質量 測算 估算 評價 軟件系統(tǒng)

【摘要】：目前,隨著現(xiàn)代信息技術與農業(yè)產業(yè)的深度融合,農業(yè)生產將變得更加智能化,這將是我國現(xiàn)代農業(yè)發(fā)展的必然趨勢。本文提出的基于圖像分析技術的小麥群體農學參數(shù)智能獲取與群體質量評價研究正是在這種現(xiàn)代農業(yè)發(fā)展的背景下展開,探求一套可以實現(xiàn)小麥生產智能化和管理高效化的新方法。研究以小麥生育進程為主線,探明了小麥苗期、越冬期、拔節(jié)期、孕穗期和成熟期主要農學參數(shù)的測算方法,并建立了小麥群體質量的評價模型,完成了小麥主要農學參數(shù)智能獲取和群體質量智能化評價系統(tǒng)。研究結果可以為小麥物聯(lián)網(wǎng)中的智能監(jiān)控系統(tǒng)提供技術支持和理論依據(jù),亦可為開發(fā)基于移動終端的智能田間測量和評價軟件提供參考。主要研究結論如下：(1)構建了大田環(huán)境下苗期麥苗智能計數(shù)的方法。這部分內容建立了基于圖像分析技術的野外環(huán)境下的麥苗智能計數(shù)方法,探明了大田環(huán)境下麥苗計數(shù)的原理,并驗證計數(shù)方法在不同密度和品種條件下的適應性。研究選取5個不同株型品種和5種不同密度的小麥苗期圖像作為研究對象,利用數(shù)碼相機垂直獲取圖像,并利用超綠特征值(ExG)將小麥從背景中分離。分析了不同重疊麥苗區(qū)域的特征參數(shù),建立了一種基于鏈碼的骨架優(yōu)化方法,并利用新骨架特征值提出了重疊區(qū)域麥苗計算公式。研究對5種不同播種密度的5個小麥品種共計250張圖像進行計數(shù)測試,結果發(fā)現(xiàn)本研究提出的麥苗計數(shù)方法能夠較好的對野外麥苗進行計數(shù),平均計數(shù)準確率達89.94%,135×104株ha-1密度樣本的計數(shù)準確率達到97.14%,在所有密度中最高,揚糯麥1號品種計數(shù)準確率達92.54%,在所有品種中最高。麥苗計數(shù)方法平均準確率89.94%,最高準確率達到99.21%,不同密度樣本計數(shù)準確率之間達到了顯著差異,而品種之間的差異沒有達到顯著水平(P0.05)。在田間苗數(shù)為120×104株ha-1至240×104株ha-1時本方法能夠得到92%以上的準確率,說明本文設計的方法在麥苗計數(shù)上是可靠的,可為田間麥苗智能計數(shù)的研究提供理論依據(jù),同樣能移植到如水稻等禾本科作物的苗數(shù)智能計算上。(2)建立了越冬、拔節(jié)和孕穗期主要農學參數(shù)的估算模型。本研究擬利用圖像分析技術建立小麥干物重、葉面積指數(shù)、莖蘗數(shù)和氮素積累量的估測模型,為這些農學參數(shù)的快速測量提供理論支持。通過不同的密度和氮肥施用量來構建具有不同農學參數(shù)的小麥群體,自群體越冬始期利用數(shù)碼相機垂直獲取冠層圖像。研究通過超綠特征值(ExG)+自適應閾值分割(Ostu)的方法去除麥田耕地背景的影響,并用圖像中小麥像素數(shù)占總像素數(shù)的比值表示蓋度,另選取8種主要的圖像特征算法提取圖像的顏色和紋理特征,利用斯皮爾曼相關分析方法分析9種特征與不同時期農學參數(shù)的相關性。利用多元逐步線性回歸方法建立基于圖像蓋度、顏色和紋理特征的農學參數(shù)估測模型。研究結果顯示,本文提出的多元線性農學參數(shù)估測模型提高了單一參數(shù)模型的模擬精度,建立的4個模型對干物重、葉面積指數(shù)、莖蘗數(shù)和氮素積累量的預測效果較好,均具有較高的R2值,較低的RMSEP和REP。對于模型構建數(shù)據(jù)集的四個農學參數(shù)預測,R2值在0.77至0.91,REP值在15.46%至22.53%；驗證數(shù)據(jù)集的R2值在0.72至0.85,REP值在17.31%至21.26%。本研究提出的多元農學參數(shù)估算模型能夠較準確的估測出小麥群體的干物重、葉面積指數(shù)、莖蘗數(shù)和氮素積累量的值。(3)設計了成熟小麥穗數(shù)的智能化計算方法。為了實現(xiàn)不同播種方式下固定區(qū)域小麥穗數(shù)的智能計算,設計了一種利用圖像分析技術實現(xiàn)大田麥穗快速計數(shù)的方法,著重分析了利用顏色特征和紋理特征分割麥穗的優(yōu)缺點和粘連區(qū)域麥穗個數(shù)的計算方法。通過對撒播和條播多個樣本圖像進行計數(shù)實驗,準確率分別為95.63%和97.07%。本研究結果說明,利用顏色特征和紋理特征均可以將麥穗從復雜的背景中提取出來,并可以通過形態(tài)學的腐蝕和膨脹以及孔洞填充算法得到麥穗的主要區(qū)域,然而利用顏色特征提取麥穗的速度遠高于利用紋理特征提取。麥穗二值圖像骨架的Harris角點能夠較好的反映粘連區(qū)域的麥穗個數(shù),Harris角點檢測算法可以用于解決麥穗計數(shù)時粘連區(qū)域麥穗個數(shù)計算。本研究提出的麥穗計數(shù)方法在撒播小麥和條播小麥上的平均準確率分別為95.63%和97.07%。本研究提出的麥穗計數(shù)方法在不同品種上的平均高于95%,且麥穗計數(shù)結果在不同品種之間沒有顯著差異,說明該大田麥穗計數(shù)方法較為可靠,可以為大田麥穗的智能化計數(shù)提供有效的參考。(4)構建了基于BP神經(jīng)網(wǎng)絡的小麥群體質量評價模型。智能化地評價群體質量對于小麥智能化生產和快速制定栽培管理方案具有積極意義,完成群體質量評價模型的構建需要進行兩部分工作：1)探明不同產量群體在不同生育期里表現(xiàn)的農學特征,明確高產群體在不同生育期的農學參數(shù)表現(xiàn)；2)構建不同生育期的小麥群體群體質量評價標準和評價模型。在第一部分研究中,試驗選擇揚糯麥1號為供試品種,采用二因素隨機區(qū)組試驗來構建不同結構的群體,設五個種植密度水平,四個氮肥施用量水平,重復兩年。研究結果如下：1)探明了產量隨種植密度變化的趨勢和高產群體的種植密度范圍；2)探明了越冬、拔節(jié)和孕穗期干物重、葉面積指數(shù)、莖蘗數(shù)、氮素積累量的變化對產量的影響和這4個農學指標在不同產量群體的區(qū)間；3)探明了產量隨穗數(shù)的變化趨勢和高產群體的穗數(shù)范圍。這部分研究是群體質量評價的依據(jù)。依據(jù)前面所探明的苗數(shù)、干物重、葉面積指數(shù)、莖蘗數(shù)、氮素積累量、麥穗數(shù)與產量的關系,構建了基于這些農學參數(shù)的小麥群體質量評價模型。研究中通過K-means聚類算法對群體等級進行劃分,以產量為標準劃分各個時期的級別,同時基于這些農學參數(shù)的模擬值構建了用于評價小麥群體質量的BP神經(jīng)網(wǎng)絡模型,各個時期評價的依據(jù)分別為：1)苗期,以苗數(shù)為依據(jù)評價群體種植密度的合理性。2)越冬、孕穗和拔節(jié)期,以干物重、葉面積指數(shù)、莖蘗數(shù)和氮素積累量為依據(jù),綜合對這幾個時期的群體質量進行評價。3)成熟期,以穗數(shù)為依據(jù),判斷群體穗數(shù)的合理性并對產量進行預測。研究解決了對小麥群體質量評價中各個農學參數(shù)與群體質量的非線性映射關系以及各個指標貢獻率的問題。研究結果顯示,研究中構建的群體質量評價模型在對苗期、越冬期、拔節(jié)期、孕穗期和成熟期的群體質量進行評價時,得到了較高的R2值和相對較低的RMSE值,說明模型可以用于評價小麥各個生育期的群體質量。該模型是后期開發(fā)群體質量評價系統(tǒng)和栽培決策的核心組成,亦可為其他作物群體質量評價提供一定的參考。(5)小麥群體農學參數(shù)測量與群體質量評價軟件系統(tǒng)的構建。軟件系統(tǒng)的構建是將此前提出算法的具體實踐,是將小麥群體智能評價方法實用化的有效途徑。本系統(tǒng)基于C/S的三層結構來開發(fā),使用Microsoft Visual Studio 2013開發(fā)平臺,MATLAB2014a圖像處理和計算機視覺工具箱,SQL Server2013數(shù)據(jù)庫完成開發(fā)。軟件系統(tǒng)實現(xiàn)了麥苗計數(shù)和麥穗計數(shù),越冬、拔節(jié)和孕穗期的莖蘗數(shù)、葉面積指數(shù)、干物重和氮素積累量的估測,以及各個生育時期群體質量的評價以及產量的預測。系統(tǒng)預留模型參數(shù)調節(jié)、品種群體質量標準添加接口和高產栽培方案添加接口,為后期在不同品種上的應用提供支持。本系統(tǒng)可為開展小麥田間智能感知和栽培決策的研究與應用提供一定的參考。
[Abstract]:At present, with the integration of modern information technology and agriculture industry depth, agricultural production will become more intelligent, this will be the inevitable trend of the development of modern agriculture in China. In this paper, the intelligent agriculture parameter of wheat based on image analysis technology for evaluation and Study on population quality is carried out in the context of the development of modern agriculture. To explore a new method of wheat production can achieve intelligent and efficient management. Research on wheat growth process as the main line, proved the wheat overwintering period, seedling stage, jointing stage, booting stage and calculation method of main agricultural maturity parameters, and establishes the evaluation model of wheat quality, complete the main agronomic parameters intelligent wheat population quality acquisition and intelligent evaluation system. The research results can provide technical support and theoretical basis for the intelligent monitoring system for wheat in the Internet of things, but also open Intelligent field measurement and evaluation software based on mobile terminal to provide the reference. The main conclusions are as follows: (1) the construction method of wheat field seedling intelligent counting environment. This part establishes the intelligent seedling counting method by image analysis technique based on field environment, proved the principle seedling counting under field environment, and validation of adaptive counting method in different density and variety conditions. The research chooses 5 different varieties and 5 different densities of wheat seedling image as the research object, the vertical image by digital camera, and the use of super green feature value (ExG) of wheat separated from the background. It analyzed the characteristic parameters of different overlapping region of wheat the establishment of a skeleton optimization method based on chain code, and use the new skeleton characteristic value calculation formula was proposed. The wheat overlapping area to study 5 kinds of different sowing density 5 wheat varieties with a total of 250 images were counting test, results showed that seedling counting method proposed in this study can count the number of wild barley, the average counting accuracy of 89.94%, 135 * 104 strains count HA-1 sample density accuracy rate reached 97.14%, the highest in all dimensions, Yang waxy wheat No. 1 varieties accurate count rate reached 92.54%, the highest in all varieties. Seedling counting method the average accuracy rate of 89.94%, the highest accuracy rate of 99.21% different density sample counting accuracy reached a significant difference, and the difference among the cultivars did not reach significant level (P0.05). The field seedling number is 120 * HA-1 to 240 * 104 strains 104 strains of HA-1 this method can obtain the accurate rate of more than 92%, shows that this design method is reliable in seedling counting, to provide a theoretical basis for research on the wheat field intelligent counting, can also be transplanted to such as The number of seedlings of rice and other cereal crops intelligent computing. (2) to establish the estimation model of main agronomic parameters of wintering, jointing and booting stage. This study intends to establish the wheat dry weight analysis technique using image, leaf area index, tiller number and nitrogen accumulation amount estimation model, to provide theoretical support for rapid measurement the agronomic parameters. The density and quantity of nitrogen fertilizer on different constructs with different agronomic parameters of wheat over wintering population groups, since the vertical access canopy image by digital camera. Through the study of super green feature value (ExG) + adaptive threshold segmentation (Ostu) effect method to remove the background and use of crop land. The image of wheat as the total number of pixels prime ratio of coverage, there were 8 main types of image feature extraction algorithm of color and texture features of the image, using the Spielman correlation analysis of 9 kinds of features and The correlation of agronomic parameters at the same time. By using multiple linear regression method based on image coverage estimation model of agronomic parameters of color and texture features. The results of the study showed that multiple linear agronomic parameters estimation model is proposed in this paper to improve the simulation accuracy of single parameter model, the 4 model of dry weight, leaf area index. Better prediction of tillers and nitrogen accumulation, have high R2 value, low RMSEP and REP. for four agronomic parameters model to construct the data set, R2 value is 0.77 to 0.91, REP value is 15.46% to 22.53%; the validation data set at R2 value of 0.72 to 0.85, REP value the model can accurately estimate the wheat dry weight estimation in multiple agronomic parameters of 17.31% to 21.26%. in this paper, leaf area index, tiller number and nitrogen accumulation value. (3) the design of mature spike number The intelligent calculation method. In order to realize the intelligent fixed area under Different Sowing Patterns in spike number calculation, design a kind of analysis method to realize fast counting of wheat field by the technology of image segmentation, analyzes the calculation method of wheat using color feature and texture feature and the advantages and disadvantages of the regional grain number. Adhesion by counting experiments to sow and drill a plurality of sample images, the accuracy rate were 95.63% and 97.07%. respectively. The results of this study show that the use of color features and texture features can be extracted from the wheat complex background, and by morphological erosion and dilation and hole filling algorithm to get the main grain area, however, the use of color feature extraction of wheat the rate is much higher than the use of texture feature extraction. The wheat two value image skeleton Harris corner can reflect a good regional wheat adhesion. The number of Harris corner detection algorithm can be used to solve the grain count number of wheat adhesion area calculation. Grain counting method proposed by this study in sowing wheat and wheat drill the average accuracy rate of respectively 95.63% and 97.07%. grain counting method proposed by this study in different species on average more than 95%, and the spike count results there was no significant difference among different varieties in wheat field, that the counting method is reliable, can provide effective reference for intelligent counting field wheat. (4) the construction of wheat quality evaluation group model based on BP neural network. The intelligent evaluation of population quality in wheat production and the rapid development of intelligent cultivation management scheme has a positive meaning, complete population quality evaluation model is constructed to two parts: 1) proved different yield group performance at different growth stages in agricultural Characteristics of high yield population in clear agronomic parameters in different growth stages; 2) construction of wheat population quality evaluation criteria and evaluation model in different stages. In the first part of the study, test Yang waxy wheat No. 1 as tested varieties, to construct different structure groups using two factor randomized block test five planting density levels, four nitrogen levels, repeated for two years. The results are as follows: 1) proved the range of planting density yield with planting density change trend and high yield population; 2) proved the wintering, jointing and booting stage of dry weight, leaf area index, tiller number, influence the accumulation of nitrogen on the yield and the 4 agronomy index in the interval of different yield groups; 3) proved the yield change trend with spike number and spike number of High-yielding Population range. This part of research is population quality evaluation according to the evidence. In front of the proven seedling number, dry weight, leaf area index, tiller number, nitrogen accumulation, the relationship between grain number and yield, construct the wheat population quality evaluation model based on the agronomic parameters. Research by K-means clustering algorithm is used to divide the population level to yield as the standard to divide each period at the same time, the simulation level, agronomy parameter values based on the BP neural network model for the evaluation of quality of wheat population was constructed based on the evaluation of each period are as follows: 1) at seedling stage, seedling number as the basis for evaluation of the rationality of.2 group planting density) wintering and jointing stage, booting stage, the dry weight, leaf area index. Tiller number and nitrogen accumulation based on the comprehensive quality evaluation of several groups during the mature period,.3) with spike number as the basis, judging the rationality of spike number and yield forecast research to solve the wheat group The problem of nonlinear mapping between the various agronomic parameters and population quality body quality evaluation and each index contribution rate. The results showed that the population quality evaluation model in the seedling stage, over wintering stage, jointing stage, booting stage and in population quality maturity evaluation, obtained a higher R2 value and the relatively low RMSE value, so the model can be used to evaluate the quality of wheat in different growth stages. The model group is the core of the late development group quality evaluation system and cultivation decision-making, but also provide some reference for other crop population quality evaluation. (5) the construction of wheat agronomic parameters measurement and population quality evaluation software system the software system is constructed. The specific practice of the previously proposed algorithm, is the effective way of practical evaluation method of wheat population intelligence. The system is based on the three layer structure of C/S The development, use Microsoft Visual Studio 2013 development platform, MATLAB2014a computer vision and image processing toolbox, SQL Server2013 database. The software system realizes counting and counting of winter wheat seedling, tiller number, jointing and booting stage, leaf area index, estimation of dry weight and nitrogen accumulation, and the assessment of population quality each growth period and yield prediction system. Reservation model parameter adjustment, breeds quality standard and cultivation scheme add add interface interface, provide support for the application in the late on different varieties. This system can provide some reference for the research and application of wheat field intelligent perception and decision to carry out the cultivation.

【學位授予單位】：揚州大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：S512.1

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