应用AgLeader®棉花产量监测仪测量花生产量:在美国两个州的调查。

Peanut Science Pub Date : 2020-06-01 DOI:10.3146/ps19-16.1
W. Porter, J. Ward, Randal K. Taylor, Chad B. Godsey
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引用次数: 2

摘要

先前的研究人员展示了将AgLeader®棉花监视器应用于花生联合收割机的能力。结果表明,当至少施加5个校准载荷时,所有试验的平均误差小于10%,可以准确地预测场权重。该项目的重点是扩展以前在佐治亚大学和其他花生光学产量监测工作中进行的工作,方法是为传感器安装一个保护性偏导板,获得多个田间重量,并使用花生销售表将产量监测产量与销售重量相关联。这项研究是由两所大学、两个州共同完成的,包括俄克拉荷马州立大学(Oklahoma State University)和密西西比州立大学(Mississippi State University)。本研究收集的数据包括多个负荷,包括产量监视器重量、田间重量、田间水分含量,以及美国农业部标准花生等级表上提供的所有信息。多州的努力使得两种主要花生类型的结合和不同土壤类型的结合成为可能。本研究的目的是制定使用、校准和调整AgLeader®棉花监测器用于花生收获的指南。通常需要五个参照购买点净重的校准负载,以使误差在可接受的范围内。结果表明,为了确保高数据有效性和跨多种收获环境的产量估算,需要进行多次局部校准。数据表明,花生类型(弗吉尼亚花生、奔跑花生和西班牙花生)和不同的土壤条件影响产量估算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Note on the Application of an AgLeader® Cotton Yield Monitor for Measuring Peanut Yield: An Investigation in Two US states.
Previous researchers demonstrated the ability to adapt an AgLeader® Cotton Monitor to a peanut combine. It was demonstrated that the field weight could be accurately predicted with average errors of less than 10% across all trials when at least five calibration loads are applied. This project focused on expanding previous work performed at the University of Georgia and other peanut optical yield monitor work by incorporating a protective deflector plate for the sensors, obtaining multiple field weights, and using the peanut sale sheets to correlate yield monitor yield to sale weight. This study was a two-university, two-state effort, including Oklahoma State University (Oklahoma), and Mississippi State University (Mississippi). Data collected during this study included multiple loads which included yield monitor weight, field weight, field moisture content, and all the information presented on the standard USDA peanut grade sheet, when available. The multi-state effort allowed for the incorporation of the two major peanut types and for the incorporation of different soil types. The goal of this study was to develop guidelines for using, calibrating, and adapting the AgLeader® Cotton Monitor for peanut harvest. Five calibration loads referenced to buy-point net weight were typically needed to bring error within acceptable limits. Results indicated that multiple local calibrations were needed to ensure high data validity and yield estimation across multiple harvest environments. The data showed that peanut type (virginia, runner and spanish) and variable soil conditions impacted yield estimation.
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