Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study
Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study
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DOI：
10.1117/1.jrs.14.024519
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发表时间：
2020-06-05
影响因子：
1.7
通讯作者：
Pethybridge, Sarah Jane
中科院分区：
文献类型：
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作者：
Hassanzadeh, Amirhossein;van Aardt, Jan;Pethybridge, Sarah Jane
农民和种植者通常使用基于作物环境和当地气象的方法来预测作物产量，其中许多是劳动密集型的。这些方法已被广泛接受，但缺乏实时和生理反馈，几乎每天的管理目的。对于大面积的作物来说，情况就是如此，例如在每年的农业市场上价值数亿美元的菜豆。我们的目的是调查油豆角产量和植物光谱和生物物理信息之间的关系，收集使用高光谱分光光度计（400至2500 nm）。实验集中在48个单一的油豆角植物（cv。亨廷顿）在生长期（69天）期间在受控的温室环境中培养。我们使用偏最小二乘回归和交叉验证方法的适用准确度和精度指标来评估两个收获阶段的预测能力，即早期收获和后期收获阶段，对我们的产量指标（豆荚重量）。四个不同的光谱数据集被用来调查是否这样的过采样，高光谱数据集可以准确和精确地模拟观测到的变异性，在决定系数（R-2）和均方根误差（RMSE）。我们的方法的目标取决于从本研究中选择光谱带的理念，即，那些最能解释产量变化的信息可以从高光谱系统中提取出来，用于更具成本效益的可操作多光谱传感器。我们的研究结果表明，最佳时期的光谱评估油豆角产量是20至25或32天收获前的早期和后期阶段，分别与最佳模型表现在一个低的RMSE（3.02克植物（-1））和高的决定系数（R-2 = 0.72）。安装在无人驾驶航空系统上的、负担得起的、波长可编程的多光谱成像仪，其波段与所确定的波段相对应，可以在收获前提供近实时和可靠的产量估计。(C)作者。由SPIE在知识共享署名4.0未移植许可下发布。
Farmers and growers typically use approaches based on the crop environment and local meteorology, many of which are labor-intensive, to predict crop yield. These approaches have found broad acceptance but lack real-time and physiological feedback for near-daily management purposes. This is true for broad-acre crops, such as snap bean, which is valued at hundreds of millions of dollars in the annual agricultural market. We aim to investigate the relationships between snap bean yield and plant spectral and biophysical information, collected using a hyperspectral spectroradiometer (400 to 2500 nm). The experiment focused on 48 single snap bean plants (cv. Huntington) in a controlled greenhouse environment during the growth period (69 days). We used applicable accuracy and precision metrics from partial least squares regression and cross-validation methods to evaluate the predictive ability of two harvest stages, namely an early-harvest and late-harvest stage, against our yield indicator (bean pod weight). Four different spectral data sets were used to investigate whether such oversampled, hyperspectral data sets could accurately and precisely model observed variability in yield, in terms of the coefficient of determination (R-2) and root-mean-square error (RMSE). The objective of our approach hinges on the philosophy that selected spectral bands from this study, i.e., those that best explain yield variability, can be downs ampled from a hyperspectral system for use in a more cost-effective, operational multispectral sensor. Our results suggested the optimal period for spectral evaluation of snap bean yield is 20 to 25 or 32 days prior to harvest for the early- and late-harvest stages, respectively, with the best model performing at a low RMSE (3.02 g plant(-1)) and a high coefficient of determination (R-2 = 0.72). An unmanned aerial systems-mounted, affordable, and wavelength-programmable multispectral imager, with bands corresponding to those identified, could provide a near real-time and reliable yield estimate prior to harvest. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.