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On-the-go yield and sugar sensing in grape harvester

机译:葡萄收割机的实时产量和糖分感应

摘要

This paper summarises the results of a joint R+D project between university and industry. The study was developed at the Alt Penedès region, in Barcelona, during the 2006 and 2007 (on 3, 22, 69 fields respectively). The quality sensors set-up in year 2007, mounted on a New Holland SB55 grape harvester, were: two load cells, one refractometer, an ambient temperature prove and a GPS antenna, while in 2006 only the load cells and the GPS performed properly. The method used for this study is as follows: 1. Data recording from GPS and Logger (the latter is use for according and digitalising the sensor signal); 2. Wireless download of data to a PC; 3. Automatic data integration in a single file; 4. Lane automatic identification based on trajectory angles, machine forward speed determination, effective time calculation, masic flow, kg/m, and total amount harvested, kg/hopper, computation of characteristic soluble solid content and temperature during harvest; 5. Data broadcasting through GPRS to the winery; 6. Comparison of transmitted data with the invoice of the winery containers. After the season was finished, a data post processing was performed in order to a assess the causes of isolated incidences that were registered in 10 fields. Also a recalibration of the sensors for future seasons was performed. At current stage R 2 of 0.9547 is found between winery and in field yield data. Beside georeference data were gathered and compare to the remote photos in “Instituto Cartográfico de Cataluña”. Site-specific yield maps and speed maps have been computed while broad soluble solid information is not available due to slight dysfunctions of the grape juice pumping system towards to the refractometer.
机译:本文总结了大学与工业界联合研发项目的成果。这项研究是在2006年和2007年期间在巴塞罗那的AltPenedès地区进行的(分别在3、22、69个领域)。 2007年安装在纽荷兰SB55葡萄收获机上的质量传感器设置为:两个称重传感器,一个折光仪,一个环境温度证明和一个GPS天线,而在2006年,只有称重传感器和GPS能够正常工作。本研究使用的方法如下:1.来自GPS和Logger的数据记录(后者用于根据传感器信号进行数字化); 2.将数据无线下载到PC; 3.单个文件中的自动数据集成; 4.基于轨迹角,机器前进速度确定,有效时间计算,质量流量,kg / m和收获总量,kg /料斗,计算特征性可溶性固形物含量和收获时温度的车道自动识别; 5.通过GPRS向酒厂广播数据; 6.将传输的数据与酿酒厂容器的发票进行比较。季节结束后,进行数据后处理,以评估10个字段中记录的孤立事件的原因。此外,还对传感器进行了重新校准以适应未来的季节。在当前阶段,酿酒厂之间和现场产量数据之间的R 2为0.9547。除地理参考数据外,还与“加泰罗尼亚加托格拉菲科研究所”中的远程照片进行了比较。已经计算出了特定地点的产量图和速度图,但是由于葡萄汁泵送系统向折光仪的轻微故障,无法获得广泛的可溶性固体信息。

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