本文選題：精密播種機　切入點：定量供種　出處：《江蘇大學》2017年碩士論文　論文類型：學位論文

【摘要】：工廠化水稻育秧設備可以提高水稻作物產(chǎn)量,氣吸振動式精密播種機作為工廠化水稻育秧設備的一種,對種子不需要分級精選、不傷種,因而具有很好的應用前景。相關研究人員已從影響其吸排種系統(tǒng)工作的因素:吸盤氣流場種子受力、振動種盤內種群的運動規(guī)律等方面進行了廣泛深入的研究,并給出了具體的建議,提升了播種效果。但現(xiàn)有播種機在播種過程中依然依靠人工加種和檢測播種效果,勞動量大、自動化程度低,針對以上問題,本文開展的主要研究工作如下:設計了一種在播種間歇時進行自動補種的定量供種裝置,以解決原有加種裝置人工重復勞動、無法實現(xiàn)自動定量供種的問題。供種裝置機械部分主要由步進電機支撐板、窩眼輪、旋轉軸、軸承及聯(lián)軸器組成,控制及執(zhí)行部分主要由PLC控制器、觸摸屏、步進電機、光電傳感器部分組成�；贓DEM進行仿真試驗與分析以確定窩眼型孔的參數(shù);根據(jù)播種機的工作過程,確定了供種裝置與吸排種系統(tǒng)在工作過程中的間歇關系,采用光電傳感器計數(shù)播種盤數(shù)、PLC控制步進電機帶動窩眼輪旋轉進行補種的方法;自動定量供種裝置可以實現(xiàn)在播種間歇時進行自動定量供種。為了檢測播種機加上自動定量供種裝置后的播種效果,驗證供種裝置設計的合理性,設計了基于機器視覺和GABP算法的播種效果檢測系統(tǒng)試驗平臺,試驗平臺硬件主要包括攝像鏡頭、光源組成的育秧盤圖像采集系統(tǒng),軟件主要包括LABVIEW圖像采集和結果顯示模塊,以及MATLAB圖像信息處理模塊。進行了播種試驗,驗證了預設的播種盤數(shù)的合理性及供種裝置在實際播種時的加種效果。以超級稻為播種對象進行了定量供種試驗,試驗結果表明:每播種10盤、15盤、20盤,由供種裝置進行加種后,播種平均合格率分別為91.96%,90.98%,90.31%�？紤]每盤播種合格率和工作效率,確定每播種15盤由供種裝置加種;在保持真空壓力、振動種盤振幅和頻率等條件不變的情況下,進行了播種檢測試驗,加種后連續(xù)播種15盤,各盤播種合格率均在90%以上,平均合格率91.30%。人工檢測和系統(tǒng)檢測的播種平均合格率分別為92.15%,88.26%,相對誤差為4.2%,檢測算法平均用時為1.118s。播種平均合格率大于90%,供種效果達到預期目標,驗證了供種裝置的合理性。
[Abstract]:Industrial rice seedling raising equipment can improve the yield of rice crops. As one of the industrial rice seedling raising equipment, the precision seeder of air suction vibration type does not need to grade and select the seeds and do not hurt the seeds. Therefore, it has a good prospect of application. Researchers have carried out extensive and in-depth studies on the factors that affect the work of the suction and drainage system, such as the force on the air flow field on the sucker, the movement law of the population in the vibrating seed disk, and so on. Some specific suggestions are given to improve the seeding effect. However, the existing seeding machines still rely on artificial seeding and testing seeding effect in the process of seeding, which has the advantages of large amount of labor and low degree of automation, aiming at the above problems. The main research work in this paper is as follows: a quantitative seed feeding device is designed to solve the problem of artificial repeated work of the original seed feeding device. The mechanical part of the seed feeding device is mainly composed of step motor support board, hole wheel, rotary shaft, bearing and coupling, and the control and execution part is mainly composed of PLC controller, touch screen, step motor, etc. According to the working process of the seeder, the intermittent relationship between the seeding device and the sucking and metering system in the working process is determined. The method of using photoelectric sensor to count the seeding plate and PLC to control the stepper motor to drive the rotation of the hole wheel to make up the seed; In order to test the seeding effect of seeding machine and automatic quantitative seed supply device, the design of seed supplying device is reasonable. The experiment platform of seeding effect detection system based on machine vision and GABP algorithm is designed. The hardware of the test platform mainly includes the image acquisition system composed of camera lens and light source. The software mainly includes LABVIEW image acquisition and result display module. And MATLAB image information processing module. Seeding experiment was carried out to verify the rationality of the preset seeding disc number and the effect of seed feeding device in actual seeding. The quantitative seeding experiment was carried out with super rice as the seeding object. The test results show that the average qualified rate of seeding is 91.96 ~ 90.98 ~ 90.98 ~ 90.31 / 10 / 10 / 10 / pan / 15 / L / 20 / L, respectively, after seeding is added by the seeding device. Considering the qualified rate and working efficiency of each tray, it is determined that the seed is added to each seeding plate by the seeding device, and the vacuum pressure is maintained. Under the condition that the vibration seed disc amplitude and frequency are not changed, the test of seeding is carried out. After continuous seeding of 15 discs, the qualified rate of seeding of each disc is above 90%. The average qualified rate was 91.30.The average qualified rate of seeding was 92.158.26, the relative error was 4.2, the average time of the detection algorithm was 1.118s, the average qualified rate of seeding was more than 90, the seed supply effect reached the expected goal, and the reasonableness of the seed supplying device was verified.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S223.2

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本文編號：1569551