Predicting Table Beet Root Yield with Multispectral UAS Imagery
Predicting Table Beet Root Yield with Multispectral UAS Imagery
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DOI：
10.3390/rs13112180
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发表时间：
2021-06
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影响因子：
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通讯作者：
R. Chancia;J. Aardt;S. Pethybridge;Daniel Cross;J. Henderson
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作者：
R. Chancia;J. Aardt;S. Pethybridge;Daniel Cross;J. Henderson
及时和准确的监测有可能简化作物管理，收获计划，并在纽约州不断增长的甜菜产业加工。我们使用无人机系统（UAS）结合多光谱成像仪监测甜菜（Beta vulgaris ssp. vulgaris）树冠在纽约在2018年和2019年生长季节。我们评估了最佳配对的反射率波段或植被指数与冠层面积预测甜菜产量成分的小样本地块使用留一交叉验证。最有前途的模式是甜菜根数和质量使用的图像在出现和冠层关闭，分别。我们创建了增强的地块，由随机组合的研究地块，以进一步利用早期冠层生长面积的重要性。我们使用2018年出现的图像，实现了R2 = 0.70和84根根（~24%）的根数均方根误差（RMSE）。当对看不见的2019年数据进行测试时，相同的模型得到了127个根（~35%）的RMSE。收获的根质量最好用树冠闭合图像建模，使用2018年数据的R2 = 0.89和RMSE = 6700 kg/ha。我们将该模型应用于2019年的全场图像，发现平均产量为41，000公斤/公顷（纽约北部的平均产量约为40，000公斤/公顷）。这项研究表明，潜在的表甜菜产量模型结合使用的辐射和冠层结构数据在早期生长阶段。这些早期生长阶段的额外图像对于开发一个强大的和通用的甜菜根产量模型至关重要，该模型可以处理在季节之间略有不同的生长阶段捕获的图像。
Timely and accurate monitoring has the potential to streamline crop management, harvest planning, and processing in the growing table beet industry of New York state. We used unmanned aerial system (UAS) combined with a multispectral imager to monitor table beet (Beta vulgaris ssp. vulgaris) canopies in New York during the 2018 and 2019 growing seasons. We assessed the optimal pairing of a reflectance band or vegetation index with canopy area to predict table beet yield components of small sample plots using leave-one-out cross-validation. The most promising models were for table beet root count and mass using imagery taken during emergence and canopy closure, respectively. We created augmented plots, composed of random combinations of the study plots, to further exploit the importance of early canopy growth area. We achieved a R2 = 0.70 and root mean squared error (RMSE) of 84 roots (~24%) for root count, using 2018 emergence imagery. The same model resulted in a RMSE of 127 roots (~35%) when tested on the unseen 2019 data. Harvested root mass was best modeled with canopy closing imagery, with a R2 = 0.89 and RMSE = 6700 kg/ha using 2018 data. We applied the model to the 2019 full-field imagery and found an average yield of 41,000 kg/ha (~40,000 kg/ha average for upstate New York). This study demonstrates the potential for table beet yield models using a combination of radiometric and canopy structure data obtained at early growth stages. Additional imagery of these early growth stages is vital to develop a robust and generalized model of table beet root yield that can handle imagery captured at slightly different growth stages between seasons.