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Using remotely controlled platform to acquire low-altitude imagery for grain crop mapping

机译：使用远程控制平台获取低空图像以进行谷物作物制图

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摘要

[Abstract]Agricultural crops exhibit within-field spatial variation. This variation partly results from relevant bio-physical and environmental factors that influence theudcrop during the growing season. The plant integrates the effects of nutrition, water, pests and disease, and displays the results in the foliage. Remote sensing techniques allow the foliage to be monitored and the crop status to be assessed.ududWhile the use of conventional remote sensing systems has found many applications in agriculture, it is constrained by a number of issues and problems related to spatial resolution, repeat cycle, minimum area acquired, timeliness of data, etc. Thus, this research explores the potential of developing and assessing low-cost sensing technologies to overcome these limitations. The specificudobjectives were to: a) identify, evaluate, and analyse the different options for a low-cost low-altitude (LCLA) remote sensing system that has potential for precision agriculture, b) develop a LCLA remote sensing system that is appropriate for use in mapping selected crop attributes (i.e. grain protein, yield, maturity and crop type), and c) evaluate the accuracy of classification and prediction of the cereal crop attributes.ududA low-cost sensor system was developed that incorporated two consumer digital still cameras. One camera captured the colour portion of the spectrum, while the other one (with the addition of a band-pass filter) captured the nearudinfrared light. Both cameras were modified to be remotely triggered and externally powered. This sensor arrangement utilised 1.0 megapixel cameras in the earlier investigations and then 5.0 megapixel cameras in most recent missions. The sensors were equally well suited to mounting on a remotely controlled aircraft or suspended beneath a helium balloon.ududVarious approaches were taken to determine and evaluate the relationships between imagery and crop attributes. Statistical methods included the use of correlation and discriminant function analysis, along with partial least squares regression. Image analysis techniques included the use of both pixel-based (supervised approach) and object-orientated (multi-resolution segmentation) udclassifications.ududThe results showed that low-cost low-altitude remote sensing systems (incorporating consumer digital cameras with helium balloons or remotely controlled aircraft) have great capacity to quantify variability in cereal grainudcrops. Excellent relationships were found between the ‘at-harvest’ yield (R2=0.902) and protein content (R2=0.660) of wheat using a single image recorded at flowering. Partial least squares regression, using the crossvalidatedudapproach, produced a stronger relationship with a prediction accuracy of 94.2% for yield and 88.5% for protein. This relationship exceeded all other studies reported in the literature.ududThe same LCLA system has also accurately discriminated (using statistical methods) between: a) different nutrition levels in a wheat crop with 75.6% of the cases correctly classified, and b) between different cereal grain species (with differing nutrition levels) with 86.3% accuracy. These classification accuracies are comparable with, or exceeding other more expensive and/or complicated methods. Attempting to discriminate using image analysisudprocedures, the pixel-based methods yielded an overall accuracy of 65.9% when classifying cereal grain crop species comprising of nine classes. When merged to six classes, the accuracy improved to 82.1%. Using an objectorientated approach has improved the overall accuracy to 81.0% for the ninecategory classification. This study also demonstrated LCLA’s ability to assessudthe various growth stages of a barley crop prior to maturity with 83.5% of cases correctly classified.ududThis study concluded that it is feasible to accurately assess selected cereal grain crop attributes using low-cost consumer technologies. The accuracies achievedudusing this system were comparable with, or exceeded, other reported studies that used more complicated and expensive sampling systems. Further work is needed to continue refining the initial work on a fully autonomous unmannedudaerial vehicle (UAV) started in the later part of this study, to extend the use of the LCLA system into broader scale applications.
机译：［摘要］农作物具有田间空间变化。这种变化部分是由于影响生长期的相关生物物理和环境因素造成的。该植物综合了营养，水，病虫害和疾病的影响，并将结果显示在叶子上。遥感技术可以监测树叶并评估农作物的状况。 ud ud尽管使用常规遥感系统已在农业中获得了许多应用，但受到许多与空间分辨率有关的问题的困扰，因此，本研究探索了开发和评估低成本传感技术以克服这些局限性的潜力。具体的非目标是：a）识别，评估和分析具有低成本农业潜力的低成本低空（LCLA）遥感系统的不同选项，b）开发适合的LCLA遥感系统用于映射选定的作物属性（即谷物蛋白，产量，成熟度和作物类型），以及c）评估谷物作物属性的分类和预测准确性。 ud ud开发了一种低成本传感器系统，该系统结合了两个消费类数码相机。一台摄像机捕获了光谱的彩色部分，而另一台摄像机（添加了一个带通滤光片）捕获了近红外光。这两款摄像机都经过修改，可以远程触发并由外部供电。这种传感器在早期的研究中使用了1.0百万像素的摄像头，在最近的任务中使用了5.0百万像素的摄像头。这些传感器同样非常适合安装在遥控飞机上或悬挂在氦气球下面。 ud ud采用了各种方法来确定和评估图像与作物属性之间的关系。统计方法包括使用相关性和判别函数分析，以及偏最小二乘回归。图像分析技术包括同时使用基于像素的（监督方法）和面向对象的（多分辨率分割） udclassifications。 ud ud结果表明，低成本的低空遥感系统（将消费类数码相机与氦气球或遥控飞机）具有量化谷物/阴茎变异性的强大能力。使用开花时记录的单个图像，发现小麦的“收获时”产量（R2 = 0.902）与蛋白质含量（R2 = 0.660）之间具有极好的关系。使用交叉验证的 udapproach方法进行的偏最小二乘回归产生了更强的关系，其预测准确率分别为产量94.2％和蛋白质88.5％。这种关系超出了文献中报道的所有其他研究。 ud ud相同的LCLA系统还已经（使用统计方法）准确地区分了：a）小麦作物中不同的营养水平，正确分类的病例占75.6％，b）谷物品种（营养水平不同）之间的准确度为86.3％。这些分类精度可与其他更昂贵和/或更复杂的方法相媲美或超越。尝试使用图像分析/过程进行区分，当对包括九类的谷物作物种类进行分类时，基于像素的方法的总体准确性为65.9％。合并为六个类别后，准确性提高到82.1％。对于九类分类，使用面向对象的方法已将整体准确性提高到81.0％。这项研究还证明了LCLA能够评估大麦作物成熟前各个生长阶段的能力，其中83.5％的情况已正确分类。 ud ud该研究得出结论，使用低成本准确评估选定的谷类谷物作物属性是可行的消费技术。使用此系统所达到的精度与使用更复杂，更昂贵的采样系统的其他报道的研究相当或超过。需要进一步的工作来继续完善在本研究的后期开始的全自动无人 UU车辆的初始工作，以将LCLA系统的使用扩展到更广泛的应用中。

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    Jensen Troy;

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  • 年度 2008
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  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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