發(fā)布時間：2018-06-10 13:54

  本文選題：聯(lián)合收獲機(jī) + 破碎率　； 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：聯(lián)合收獲機(jī)是我國收獲水稻和小麥的主要農(nóng)業(yè)機(jī)械設(shè)備,近幾年得到飛速的發(fā)展。但是國內(nèi)聯(lián)合收獲機(jī)智能化程度相對較低,普遍缺乏工作參數(shù)與作業(yè)性能監(jiān)測裝置,作業(yè)效率依賴機(jī)手的熟練程度,且操縱強(qiáng)度大,堵塞故障頻發(fā)。在聯(lián)合收獲機(jī)作業(yè)過程中發(fā)生谷物破碎率和含雜率超標(biāo)的情況時,由于缺少在線監(jiān)測方法、駕駛?cè)藛T經(jīng)驗(yàn)不足等原因不能及時調(diào)整相關(guān)工作參數(shù),一方面會給農(nóng)民帶來直接的經(jīng)濟(jì)損失,另一方面,農(nóng)民用破碎率較高的谷物育種時,發(fā)芽率較低,破碎嚴(yán)重的會影響下季的產(chǎn)量。針對上述情況,本文著重研究聯(lián)合收獲機(jī)谷物破碎率與含雜率監(jiān)測方法,并研制監(jiān)測系統(tǒng)對聯(lián)合收獲機(jī)糧箱中谷物的破碎率與含雜率進(jìn)行監(jiān)測,監(jiān)測結(jié)果通過聯(lián)合收獲機(jī)駕駛室內(nèi)的顯示屏顯示,為駕駛?cè)藛T合理設(shè)置聯(lián)合收獲機(jī)相關(guān)工作參數(shù)提供依據(jù)。本文主要研究內(nèi)容如下:(1)研究谷物破碎率與含雜率的監(jiān)測方法。根據(jù)聯(lián)合收獲機(jī)的工作流程,分析破碎率含雜率產(chǎn)生原因。并提出利用圖像處理技術(shù)監(jiān)測聯(lián)合收獲機(jī)谷物破碎率與含雜率的方法,通過理論分析得出破碎率與含雜率的計算模型。(2)對谷物破碎率與含雜率監(jiān)測系統(tǒng)進(jìn)行總體設(shè)計,包括系統(tǒng)硬件設(shè)計及系統(tǒng)軟件設(shè)計。系統(tǒng)硬件設(shè)計包括谷物在線采集裝置的設(shè)計、采集裝置照明系統(tǒng)的設(shè)計、圖像采集模塊的選擇及系統(tǒng)處理器模塊的選擇。其中,谷物在線采集裝置選擇安裝在聯(lián)合收獲機(jī)升運(yùn)器出口處,并通過微型電磁鐵控制其底板的閉合,保證谷物采集裝置能夠周期性地采集并釋放谷物。(3)研究出谷物破碎率與含雜率監(jiān)測算法。著重分析谷物圖像分割算法,提出了利用K-Means算法對破碎谷物與雜余進(jìn)行粗提取,再通過分水嶺算法進(jìn)行細(xì)分割;提取出雜余與谷物的特征值,作為BP神經(jīng)網(wǎng)絡(luò)模型輸入?yún)?shù),識別出雜余、破碎谷物與完好谷物;同時針對設(shè)計的算法,分別在Windows平臺與ARM平臺進(jìn)行了軟件設(shè)計,并給出了具體實(shí)現(xiàn)步驟及部分代碼。(4)在聯(lián)合收獲機(jī)試驗(yàn)臺上進(jìn)行了臺架實(shí)驗(yàn),驗(yàn)證了系統(tǒng)裝置以及算法的可行性。同時,對比不同圖像傳感器的性價比以及采集的圖像質(zhì)量,分別采用攝像頭與工業(yè)相機(jī)在臺架試驗(yàn)中采集圖像,得到兩種情況下破碎率與含雜率的識別率。臺架試驗(yàn)表明,采用工業(yè)相機(jī)采集谷物圖像時,破碎率與含雜率的平均識別率分別為88.96%、88.71%;采用攝像頭采集谷物圖像時,破碎率與含雜率的平均識別率分別為68.76%、66.6%。(5)將設(shè)計的算法移植到嵌入式平臺,并將基于嵌入式平臺的的谷物破碎率與含雜率監(jiān)測系統(tǒng)安裝在聯(lián)合收獲機(jī)上,進(jìn)行田間在線監(jiān)測試驗(yàn)。試驗(yàn)表明,聯(lián)合收獲機(jī)谷物破碎率、含雜率監(jiān)測系統(tǒng)的谷物破碎率平均識別率為86.63%;忽略毫米級的微小雜余后,谷物含雜率的平均識別率為85.62%。谷物破碎率與含雜率的監(jiān)測結(jié)果能夠通過聯(lián)合收獲機(jī)駕駛室內(nèi)的顯示屏顯示,為駕駛?cè)藛T及時調(diào)整聯(lián)合收獲機(jī)工作參數(shù)提供依據(jù),滿足了聯(lián)合收獲機(jī)監(jiān)測系統(tǒng)設(shè)計的預(yù)期要求,為實(shí)現(xiàn)聯(lián)合收獲機(jī)自動化控制提供了技術(shù)支撐。
[Abstract]:Combined harvester is the main agricultural machinery equipment for harvesting rice and wheat in China, and it has developed rapidly in recent years. However, the intelligent degree of combined harvesters in China is relatively low, and there is a general lack of working parameters and performance monitoring devices. The operation efficiency depends on the proficiency of the machine hand, and the operation intensity is large and the failure frequency is frequent. When the grain breakage rate and the impurity content of the harvester are exceeding the standard, the lack of on-line monitoring methods and the lack of drivers' experience can not adjust the relevant working parameters in time. On the one hand, it will bring direct economic losses to the farmers. On the other hand, the farmers have a low germination rate when the grain breeding is high in broken rate. Serious breakage affects the production of the next season. In view of the above conditions, this paper focuses on the monitoring methods of the grain breakage rate and impurity content of the combined harvester, and develops the monitoring system to monitor the crushing rate and impurity of the grain in the combined harvester, and the monitoring results are shown by the display of the combined harvester driver's driver's display screen. The main research contents of this paper are as follows: (1) the monitoring methods of grain breaking rate and impurity ratio are studied. According to the working flow of the combined harvester, the reasons of the rate of breakage are analyzed and the image processing technology is put forward to monitor the grain breaking rate and impurity rate of the combined harvester. The calculation model of crushing rate and impurity rate is obtained through theoretical analysis. (2) overall design of grain crushing rate and impurity content monitoring system, including system hardware design and system software design. The system hardware design includes the design of grain on-line collection device, the design of the lighting system of the collection device, and the selection of the image acquisition module. Selection and selection of the system processor module. Among them, the grain collection device is chosen to be installed at the outlet of the hoisting machine of the combined harvester and controls the closure of the floor by the micro electromagnet to ensure that the grain collection device can collect and release grain periodically. (3) the monitoring algorithm of grain breakage rate and impurity rate is studied. In the image segmentation algorithm, the K-Means algorithm is used to extract the broken grain and miscellaneous residual, and then the segmentation is carried out by the watershed algorithm, and the characteristic values of the surplus and grain are extracted. As the input parameters of the BP neural network model, the miscellaneous and broken grain and the perfect grain are identified. At the same time, the design algorithm is in the Windows level, respectively. The software design of the platform and ARM platform is carried out, and the concrete implementation steps and part of the code are given. (4) the bench test is carried out on the joint harvester test platform, and the feasibility of the system device and the algorithm is verified. At the same time, the camera and the industrial camera are used to compare the cost performance of different image sensors and the quality of the collected images. In the bench test, the fragmentation rate and the rate of identification were obtained in two cases. The bench test showed that the average recognition rate of crushing rate and heterozygosity was 88.96% and 88.71%, respectively, when the industrial camera was used to collect grain images, and the average recognition rate of crushing rate and heterozygosity was 68.76% and 66. respectively when using the camera to collect grain images. 6%. (5) transplanted the design algorithm to the embedded platform, and installed the grain breakage rate and heterozygosity monitoring system based on the embedded platform on the joint harvester to carry out field on-line monitoring test. The experiment showed that the average recognition rate of grain breakage rate of the combined harvester was 86.63%, and that of the miscellaneous rate monitoring system was negligible. The average recognition rate of grain heterozygosity is 85.62%. grain breaking rate and heterozygosity, which can be shown by the monitor of the combined harvester in the driver's driving room. It provides the basis for the driver to adjust the working parameters of the combined harvester in time, and satisfies the expected requirements of the design of the combined harvester monitoring system. Now the automatic control of combine harvester provides technical support.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S225.3;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 尚會超;楊銳;段夢珍;段曉偉;張洪斌;;機(jī)器視覺照明系統(tǒng)的關(guān)鍵技術(shù)分析[J];中原工學(xué)院學(xué)報;2016年03期

2 劉瓊;史諾;;基于Lab和YUV顏色空間的農(nóng)田圖像分割方法[J];國外電子測量技術(shù);2015年04期

3 吳春冬;張文建;李衍佳;邵華;;基于LabVIEW的汽輪機(jī)轉(zhuǎn)子動葉扭轉(zhuǎn)量測量系統(tǒng)設(shè)計[J];工具技術(shù);2015年03期

4 陳慶文;韓增德;崔俊偉;王國新;喬曉東;張子瑞;韓科立;甘邦興;;自走式谷物聯(lián)合收割機(jī)發(fā)展現(xiàn)狀及趨勢分析[J];中國農(nóng)業(yè)科技導(dǎo)報;2015年01期

5 張宏;馬巖;李勇;張銳利;張學(xué)軍;張銳;;基于遺傳BP神經(jīng)網(wǎng)絡(luò)的核桃破裂功預(yù)測模型[J];農(nóng)業(yè)工程學(xué)報;2014年18期

6 白人樸;;對“十三五”我國農(nóng)業(yè)機(jī)械化發(fā)展的思考[J];農(nóng)村牧區(qū)機(jī)械化;2014年04期

7 白人樸;;對“十三五”我國農(nóng)機(jī)化發(fā)展的思考[J];農(nóng)機(jī)科技推廣;2014年08期

8 侯遠(yuǎn)韶;;機(jī)器視覺系統(tǒng)中光源的選擇[J];洛陽師范學(xué)院學(xué)報;2014年08期

9 孫惠杰;鄧廷權(quán);李艷超;;改進(jìn)的分水嶺圖像分割算法[J];哈爾濱工程大學(xué)學(xué)報;2014年07期

10 高俊梅;黃忠文;;圖像處理技術(shù)在農(nóng)業(yè)上的應(yīng)用[J];農(nóng)業(yè)網(wǎng)絡(luò)信息;2014年05期

相關(guān)博士學(xué)位論文 前5條

1 李武斌;熱軋圓鋼表面缺陷視覺在線檢測算法研究[D];山東大學(xué);2013年

2 陳俊琰;梳棉機(jī)棉網(wǎng)質(zhì)量計算機(jī)視覺檢測系統(tǒng)研究[D];東華大學(xué);2013年

3 楊蜀秦;農(nóng)作物籽粒的圖像處理和識別方法研究[D];西北農(nóng)林科技大學(xué);2012年

4 王吉權(quán);BP神經(jīng)網(wǎng)絡(luò)的理論及其在農(nóng)業(yè)機(jī)械化中的應(yīng)用研究[D];沈陽農(nóng)業(yè)大學(xué);2011年

5 毛文華;基于機(jī)器視覺的田間雜草識別技術(shù)研究[D];中國農(nóng)業(yè)大學(xué);2004年

相關(guān)碩士學(xué)位論文 前10條

1 徐玉冰;基于機(jī)器視覺的火花塞墊圈缺陷檢測系統(tǒng)設(shè)計[D];中北大學(xué);2016年

2 吳飛龍;基于機(jī)器視覺的校車安全監(jiān)測系統(tǒng)[D];浙江大學(xué);2016年

3 倪舟;蔬菜識別算法研究[D];東南大學(xué);2015年

4 袁e，

本文編號：2003446