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A Low-Cost, Efficient Strawberry Yield MonitoringSystem

机译:低成本,高效的草莓产量监测系统

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The determination of yield distribution within a farm field is crucial to more efficient and cost effective precision management. Obtaining yield map data is not an established procedure for most hand-harvested crops. Efforts to produce such data forfruit trees work either at the bin-level, use expensive technology, or require changes in workers' harvesting activities. We propose to convert the carts used for picking strawberries, which in California represent a 2 billion dollar crop, into affordable and easy to operate yield monitoring devices. A prototype cart was built and instrumented with an Arduino microcontroller, and load cells to measure the weight of the harvested strawberries as the tray filled up with fruits in real-time. Additionally,an attitude and heading reference system (AHRS) was included to compensate the weight when the load cells were tilted. An off-the-shelf GPS module was added to collect location data, as well as a relatively inexpensive real-time kinematic (RTK) GPS module to provide ground truth location data.A field experiment was carried out and as a worker picked and placed the fruits in the cart, the weight data was measured and stored in a solid-state (SD) memory card, along with GPS position data. After harvest, ayield map was generated for an approximately 0.12 ha area plot of the field. Load cell measurements showed a mean absolute percentage error of 2.6% compared to the weights recorded by a digital scale. This error was reduced to 1.6% by employing a correction factor to the load cell data.
机译:农田内产量分布的测定至关重要,对更有效和经济高效的精确管理至关重要。获得收益率地图数据不是大多数手工收获作物的既定程序。在班级级别生产这些数据的努力,使用昂贵的技术,使用昂贵的技术,或需要工作人员收获活动的变化。我们建议将用于采摘草莓的购物车转换为加利福尼亚州,该购物中心代表20亿美元的作物,以实惠且易于操作的收益监测设备。使用Arduino微控制器建造和仪器的原型推车,并称重电池,以测量收获的草莓的重量,因为托盘实时填充水果。另外,包括姿态和前线参考系统(AHRS)以补偿负载电池倾斜时的重量。添加了搁板的GPS模块以收集位置数据,以及相对便宜的实时运动(RTK)GPS模块,以提供地面真理位置数据。进行现场实验,并作为拾取和放置的工人推车中的果实,重量数据被测量并存储在固态(SD)存储卡中,以及GPS位置数据。收获后,为该字段的大约0.12公顷的区域图产生Ayield Map。与数字尺度记录的重量相比,称重传感器测量显示为2.6%的平均绝对百分比误差。通过使用校正因子对负载小区数据来减少到1.6%的错误。

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