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3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data

机译:用于作物高度评估的低成本移动设备的3D参与式感应-与地面激光扫描数据的比较

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摘要

The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devices, offering new and efficient opportunities for agricultural monitoring. This study presents a methodological approach for crop height assessment based on PS. In-field crop height variations of a maize field in Heidelberg, Germany, are gathered with smartphones and handheld GPS devices by 19 participants. The comparison of crop height values measured by the participants to reference data based on terrestrial laser scanning (TLS) results in R2 = 0.63 for the handheld GPS devices and R2 = 0.24 for the smartphone-based approach. RMSE for the comparison between crop height models (CHM) derived from PS and TLS data is 10.45 cm (GPS devices) and 14.69 cm (smartphones). Furthermore, the results indicate that incorporating participants’ cognitive abilities in the data collection process potentially improves the quality data captured with the PS approach. The proposed PS methods serve as a fundament to collect agricultural parameters on field-level by incorporating local people. Combined with other methods such as remote sensing, PS opens new perspectives to support agricultural development.
机译:当地农业知识的整合加深了对复杂现象的理解,例如气候变异性,作物产量与营养不良之间的联系。参与式传感(PS)是一种概念,它使外行可以轻松地使用标准的低成本移动设备收集地理数据,从而为农业监测提供新的高效机会。这项研究提出了一种基于PS的作物高度评估方法学方法。 19个参与者使用智能手机和手持GPS设备收集了德国海德堡玉米田的田间作物高度变化。参与者根据地面激光扫描(TLS)与参考数据测得的作物高度值进行比较,得出手持GPS设备的R 2 = 0.63,R 2 =基于智能手机的方法为0.24。从PS和TLS数据得出的作物高度模型(CHM)之间进行比较的RMSE为10.45 cm(GPS设备)和14.69 cm(智能手机)。此外,结果表明,将参与者的认知能力纳入数据收集过程可能会改善PS方法捕获的质量数据。拟议的PS方法是通过吸收当地人来收集田间农业参数的基础。 PS与遥感等其他方法结合,为支持农业发展开辟了新视野。

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