联合收获机粮箱内稻谷含杂率传感器采样盒设计（英文）
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摘要
针对收割过程中通常只在开机时设置一次作业参数,不能经常停机观察籽粒的清洁度进而随时调节作业参数这一问题,需要研制一种能够自动获取联合收获机作业过程中含杂率数据的装置。但是,迄今为止,在收获过程中监测籽粒含杂率的研究还处于探索阶段。针对以上问题,结合联合收获机作业过程中谷物的流变特性,该文设计了一种谷物含杂率传感器,通过电磁铁(弹簧)控制挡板的伸出(复位),以使采样盒可视玻璃窗口前聚集(卸载)谷物,通过采样盒内CMOS相机获取数字图像,应用数字图像处理技术获得含杂率。依据图像直方图优化LED光源的照射及安装方法以获取高质量图像,分析了电磁铁提供的拉力和弹簧提供的推力、采样盒入口尺寸,采用全局阈值迭代算法,使用三个迭代步提取稻谷、茎秆和细柄连通域,计算每个连通域的像素,最终从形态上识别了谷物和杂质,同时测量稻谷千粒质量、茎秆面密度和细柄线密度,建立了计算谷物杂质质量的数学模型。结果表明:在采样盒内两侧各安装2个LED,间接照射视窗时获取的图像质量较好,有效避免了图像强度直方图中出现的峰值;额定拉力为60 N的电磁铁可以提供足够的拉力;采用线径为1 mm的弹簧能提供足够的推力,由于在收割过程中出现的短秸秆和细柄的长度在10 mm到30 mm之间,因此采样盒的设计高度为95.7 mm,宽度为76.5 mm,入口宽度为31.9 mm,视窗长度为57.1 mm,宽度为57.4 mm,如此稻谷、秸秆和细柄可以顺利流入采样盒,并且在视窗中可以看到约200粒谷粒。在不同含杂率下进行了籽粒含杂率传感器监测精度的台架试验,结果表明:该装置监测的籽粒含杂率与人工获得的籽粒含杂率具有一致的变化趋势,能够监测在0～2.88%范围内的籽粒含杂率。为了满足田间作业监测需求,同时设计了采样盒的防水防尘罩,为相机提供一个稳定抓取图像的工作环境。于2017年11月17日,在苏州九里湖进行了不同喂入量下含杂率监测试验,试验使用Yamma4LZ2.5联合收割机,结果表明,田间试验的相对误差在9.44%和19.67%之间。该研究可为田间收获过程中自动获取谷物含杂率提供参考。
The optimized operational parameters of a combine harvester are often set once at the beginning of the operation which makes it very inconvenient to observe the grain purity and adjust these parameters frequently during the harvesting process. It is imperative to develop an instrument which can obtain grain impurity data for optimizing operational parameters during the harvesting process automatically. Based the purpose above, a grain impurity(GI) sensor was designed. The sensor consisted of electromagnet for the extension control of choke panel, spring for the reset of choke panel, visual glass window, choke panel, light resource and camera which is used for image capture of grains gathered by choke panel. Then the captured image was processed for calculating the grain impurity. The installation position and illumination type of LED light source was optimized to obtain high-quality images according to the image histogram. The pulling force applied to the choke panel by the electromagnet, the thrust force applied to the choke panel by the spring and the entrance size of sampling box were analyzed. The results showed that illuminating the visual window indirectly with two LEDs installed on both sides of the sampling box was a better way, with the peak in the image intensity histogram avoided effectively. The electromagnet with a rated pull force of 60 N could provide enough pulling force and the spring with diameter of 1 mm could provide enough thrust force. Due to the fact that the short stalks and straw during harvesting ranged from 10 to 30 mm in length, the sampling box with height of 95.7 mm and width of 76.5 mm was designed. And the inlet width was 31.9 mm and the visual window' length and width wan 57.1 and 57.4 mm respectively. Therefore, the grain, stalk and straw could flow into the sampling box smoothly and about 200 grain kernels could be seen in the visual window. The monitoring accuracy of the sensor was verified with different impurity contents in. bench test and the results showed that the device was capable of monitoring the grain impurity content in the range of 0-2.88%, whose change trend was consistent with the artificial grain impurity content. A dust-proof cover was designed to provide a stable working environment for the camera in the field. And the field experiments were conducted with YAMMA 4 LZ2.5 combine harvester in Jiuli Lake in city of Suzhou, on Nov.17, 2017. The results showed that the relative errors of grain impurity in field test were between 9.44% and 19.67%.
The optimized operational parameters of a combine harvester are often set once at the beginning of the operation which makes it very inconvenient to observe the grain purity and adjust these parameters frequently during the harvesting process. It is imperative to develop an instrument which can obtain grain impurity data for optimizing operational parameters during the harvesting process automatically. Based the purpose above, a grain impurity(GI) sensor was designed. The sensor consisted of electromagnet for the extension control of choke panel, spring for the reset of choke panel, visual glass window, choke panel, light resource and camera which is used for image capture of grains gathered by choke panel. Then the captured image was processed for calculating the grain impurity. The installation position and illumination type of LED light source was optimized to obtain high-quality images according to the image histogram. The pulling force applied to the choke panel by the electromagnet, the thrust force applied to the choke panel by the spring and the entrance size of sampling box were analyzed. The results showed that illuminating the visual window indirectly with two LEDs installed on both sides of the sampling box was a better way, with the peak in the image intensity histogram avoided effectively. The electromagnet with a rated pull force of 60 N could provide enough pulling force and the spring with diameter of 1 mm could provide enough thrust force. Due to the fact that the short stalks and straw during harvesting ranged from 10 to 30 mm in length, the sampling box with height of 95.7 mm and width of 76.5 mm was designed. And the inlet width was 31.9 mm and the visual window' length and width wan 57.1 and 57.4 mm respectively. Therefore, the grain, stalk and straw could flow into the sampling box smoothly and about 200 grain kernels could be seen in the visual window. The monitoring accuracy of the sensor was verified with different impurity contents in. bench test and the results showed that the device was capable of monitoring the grain impurity content in the range of 0-2.88%, whose change trend was consistent with the artificial grain impurity content. A dust-proof cover was designed to provide a stable working environment for the camera in the field. And the field experiments were conducted with YAMMA 4 LZ2.5 combine harvester in Jiuli Lake in city of Suzhou, on Nov.17, 2017. The results showed that the relative errors of grain impurity in field test were between 9.44% and 19.67%.
引文
[1]Liang Z,Li Y,Xu L,et al.Optimum design of an array structure for the grain loss sensor to upgrade its resolution for harvesting rice in a combine harvester[J].Biosystems Engineering,2017,157:24-34.
[2]Wang Gang,Guan Zhuohuai,Mu Senlin,et al.Optimization of operating parameter and structure for seed thresher device for rape combine harvester[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(24):52-57.(in Chinese with English abstract)
[3]Chen Jin,Wang Yuehong,Lian Yi,et al.Development of on-line monitoring device of grain moisture content in combine harvester with high frequency capacitance[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(10):36-45.(in Chinese with English abstract)
[4]Omid M,Lashgari M,Mobli H,et al.Design of fuzzy logic control system incorporating human expert knowledge for combine harvester[J].Expert Systems with Applications,2010,37(10):7080-7085.
[5]Xie Y,Alleyne A G,Greer A,et al.Fundamental Limits in Combine Harvester Header Height Control[C]//American Control Conference.IEEE,2011:345031-345038.
[6]Craessaerts G,Baerdemaeker J D,Missotten B,et al.Fuzzy control of the cleaning process on a combine harvester[J].Biosystems Engineering,2010,106(2):103-111.
[7]Gregory J M.Modelling applications in agricultural engineering:Combine model for grain threshing[J].Mathematical&Computer Modelling An International Journal,1988,11:506-509.
[8]Miu P I,Kutzbach H D.Modeling and simulation of grain threshing and separation in threshing units:Part I[J].Computers&Electronics in Agriculture,2008,60(1):96-104.
[9]Myhan R,Jachimczyk E.Grain separation in a straw walker unit of a combine harvester:Process model[J].Biosystems Engineering,2016,145:93-107.
[10]Craessaerts G,Saeys W,Missotten B,et al.A genetic input selection methodology for identification of the cleaning process on a combine harvester,Part I:Selection of relevant input variables for identification of the sieve losses[J].Biosystems Engineering,2007,98(2):166-175.
[11]Xie G D,Chen S B,Qi W H,et al.A multidisciplinary and integrated study of rice precision farming[J].Chinese Geographical Science,2003,13(1):9-14.
[12]Hiregoudar S,Udhaykumar R,Ramappa K T,et al.Artificial neural network for assessment of grain losses for paddy combine harvester a novel approach[J].Communications in Computer&Information Science,2011,140:221-231.
[13]Jahari,Yamamoto,Miyamoto,et al.Double lighting machine vision system to monitor harvested paddy grain quality during head-feeding combine harvester operation[J].Machines,2015,3(4):352-363.
[14]Zhao Z,Li Y,Chen J,et al.Grain separation loss monitoring system in combine harvester[J].Computers and Electronics in Agriculture,2011,76(2):183-188.
[15]Liang Z,Li Y,Xu L,et al.Sensor for monitoring rice grain sieve losses in combine harvesters[J].Biosystems Engineering,2016,147:51-66.
[16]Li Yaoming,Chen Yi,Zhao Zhan,et al.Monitoring method and device for cleaning loss of combine harvester[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(Supp.2):7-11.(in Chinese with English abstract)
[17]Zhao Z,Li Y,Liang Z,et al.Optimum design of grain impact sensor utilising polyvinylidene fluoride films and a floating raft damping structure[J].Biosystems Engineering,2012,112(3):227-235.
[18]Liang Z,Li Y,Xu L,et al.Optimum design of an array structure for the grain loss sensor to upgrade its resolution for harvesting rice in a combine harvester[J].Biosystems Engineering,2017,157:24-34.
[19]Liang Z,Li Y,Zhao Z,et al.Optimum design of grain sieve losses monitoring sensor utilizing partial constrained viscoelastic layer damping(PCLD)treatment[J].Sensors&Actuators A Physical,2015,233:71-82.
[20]Liang Z,Li Y,Xu L,et al.Sensor for monitoring rice grain sieve losses in combine harvesters[J].Biosystems Engineering,2016,147:51-66.
[21]Bebis G,Egbert D,Shah M.Review of computer vision education[J].IEEE Transactions on Education,2003,46(1):2-21.
[22]Brosnan T,Sun D W.Improving quality inspection of food products by computer vision:A review[J].Journal of Food Engineering,2004,61(1):3-16.
[23]Georg H,Guth N,Bockisch F J.Machine vision for the automatic measurement of broken grain fraction 1[J].IFACProceedings Volumes,1995,28(6):139-142.
[24]Jahari M,Yamamoto K,Miyamoto M,et al.Monitoring harvested paddy during combine harvesting using a machine vision-double lighting system[J].Engineering in Agriculture Environment&Food,2016,10(2):140-149.DOI:10.1016/j.eaef.2016.12.001
[25]Shoulong X,Shuliang Z,Youjun H.γ-Ray detection using commercial off-the-shelf CMOS and CCD image sensors[J].IEEE Sensors Journal,2017,17(20):6599-6604.
[26]Sofu M M,Er O,Kayacan M C.Design of an automatic apple sorting system using machine vision[J].Computers&Electronics in Agriculture,2016,127:395-405.
[27]Li N,Ye J,Ji Y,et al.Saliency Detection on Light Field[J].Transactions on pattern analysis and machine intelligence,2017,39(8):1605-1616.
[28]Liao K W,Lee Y T.Detection of rust defects on steel bridge coatings via digital image recognition[J].Automation in Construction,2016,71:294-306.
[29]Sun Z,Feng W,Zhao Q,et al.Brightness preserving image enhancement based on a gradient and intensity histogram[J].Journal of Electronic Imaging,2015,24(5):053006.
[30]Zhou S,Zhang F,Siddique M A.Range limited peakseparate fuzzy histogram equalization for image contrast enhancement[J].Multimedia Tools&Applications,2015,74(17):6827-6847.
[2]Wang Gang,Guan Zhuohuai,Mu Senlin,et al.Optimization of operating parameter and structure for seed thresher device for rape combine harvester[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(24):52-57.(in Chinese with English abstract)
[3]Chen Jin,Wang Yuehong,Lian Yi,et al.Development of on-line monitoring device of grain moisture content in combine harvester with high frequency capacitance[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(10):36-45.(in Chinese with English abstract)
[4]Omid M,Lashgari M,Mobli H,et al.Design of fuzzy logic control system incorporating human expert knowledge for combine harvester[J].Expert Systems with Applications,2010,37(10):7080-7085.
[5]Xie Y,Alleyne A G,Greer A,et al.Fundamental Limits in Combine Harvester Header Height Control[C]//American Control Conference.IEEE,2011:345031-345038.
[6]Craessaerts G,Baerdemaeker J D,Missotten B,et al.Fuzzy control of the cleaning process on a combine harvester[J].Biosystems Engineering,2010,106(2):103-111.
[7]Gregory J M.Modelling applications in agricultural engineering:Combine model for grain threshing[J].Mathematical&Computer Modelling An International Journal,1988,11:506-509.
[8]Miu P I,Kutzbach H D.Modeling and simulation of grain threshing and separation in threshing units:Part I[J].Computers&Electronics in Agriculture,2008,60(1):96-104.
[9]Myhan R,Jachimczyk E.Grain separation in a straw walker unit of a combine harvester:Process model[J].Biosystems Engineering,2016,145:93-107.
[10]Craessaerts G,Saeys W,Missotten B,et al.A genetic input selection methodology for identification of the cleaning process on a combine harvester,Part I:Selection of relevant input variables for identification of the sieve losses[J].Biosystems Engineering,2007,98(2):166-175.
[11]Xie G D,Chen S B,Qi W H,et al.A multidisciplinary and integrated study of rice precision farming[J].Chinese Geographical Science,2003,13(1):9-14.
[12]Hiregoudar S,Udhaykumar R,Ramappa K T,et al.Artificial neural network for assessment of grain losses for paddy combine harvester a novel approach[J].Communications in Computer&Information Science,2011,140:221-231.
[13]Jahari,Yamamoto,Miyamoto,et al.Double lighting machine vision system to monitor harvested paddy grain quality during head-feeding combine harvester operation[J].Machines,2015,3(4):352-363.
[14]Zhao Z,Li Y,Chen J,et al.Grain separation loss monitoring system in combine harvester[J].Computers and Electronics in Agriculture,2011,76(2):183-188.
[15]Liang Z,Li Y,Xu L,et al.Sensor for monitoring rice grain sieve losses in combine harvesters[J].Biosystems Engineering,2016,147:51-66.
[16]Li Yaoming,Chen Yi,Zhao Zhan,et al.Monitoring method and device for cleaning loss of combine harvester[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(Supp.2):7-11.(in Chinese with English abstract)
[17]Zhao Z,Li Y,Liang Z,et al.Optimum design of grain impact sensor utilising polyvinylidene fluoride films and a floating raft damping structure[J].Biosystems Engineering,2012,112(3):227-235.
[18]Liang Z,Li Y,Xu L,et al.Optimum design of an array structure for the grain loss sensor to upgrade its resolution for harvesting rice in a combine harvester[J].Biosystems Engineering,2017,157:24-34.
[19]Liang Z,Li Y,Zhao Z,et al.Optimum design of grain sieve losses monitoring sensor utilizing partial constrained viscoelastic layer damping(PCLD)treatment[J].Sensors&Actuators A Physical,2015,233:71-82.
[20]Liang Z,Li Y,Xu L,et al.Sensor for monitoring rice grain sieve losses in combine harvesters[J].Biosystems Engineering,2016,147:51-66.
[21]Bebis G,Egbert D,Shah M.Review of computer vision education[J].IEEE Transactions on Education,2003,46(1):2-21.
[22]Brosnan T,Sun D W.Improving quality inspection of food products by computer vision:A review[J].Journal of Food Engineering,2004,61(1):3-16.
[23]Georg H,Guth N,Bockisch F J.Machine vision for the automatic measurement of broken grain fraction 1[J].IFACProceedings Volumes,1995,28(6):139-142.
[24]Jahari M,Yamamoto K,Miyamoto M,et al.Monitoring harvested paddy during combine harvesting using a machine vision-double lighting system[J].Engineering in Agriculture Environment&Food,2016,10(2):140-149.DOI:10.1016/j.eaef.2016.12.001
[25]Shoulong X,Shuliang Z,Youjun H.γ-Ray detection using commercial off-the-shelf CMOS and CCD image sensors[J].IEEE Sensors Journal,2017,17(20):6599-6604.
[26]Sofu M M,Er O,Kayacan M C.Design of an automatic apple sorting system using machine vision[J].Computers&Electronics in Agriculture,2016,127:395-405.
[27]Li N,Ye J,Ji Y,et al.Saliency Detection on Light Field[J].Transactions on pattern analysis and machine intelligence,2017,39(8):1605-1616.
[28]Liao K W,Lee Y T.Detection of rust defects on steel bridge coatings via digital image recognition[J].Automation in Construction,2016,71:294-306.
[29]Sun Z,Feng W,Zhao Q,et al.Brightness preserving image enhancement based on a gradient and intensity histogram[J].Journal of Electronic Imaging,2015,24(5):053006.
[30]Zhou S,Zhang F,Siddique M A.Range limited peakseparate fuzzy histogram equalization for image contrast enhancement[J].Multimedia Tools&Applications,2015,74(17):6827-6847.