Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean
Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean
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DOI：
10.3390/rs13163241
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发表时间：
2021-08-01
期刊：
影响因子：
5
通讯作者：
Pethybridge, Sarah J.
中科院分区：
文献类型：
--
作者：
Hassanzadeh, Amirhossein;Zhang, Fei;Pethybridge, Sarah J.
准确、精确和及时地估计作物产量是种植者主动管理作物生长和预测收获物流能力的关键。这种产量预测通常基于多参数模型和现场取样。在这里，我们调查的温室研究的扩展，低空无人机系统（UAS）。我们的主要目标是调查菜豆作物（菜豆）产量使用成像光谱（高光谱成像）在可见光到近红外（VNIR; 400-1000 nm）区域通过无人机。我们的目的是解决作物产量建模的问题，通过识别光谱特征解释产量和评估最佳的时间段，准确的产量预测，早期。我们引入了一个名为Jostar的Python库，用于光谱特征选择。在Jostar中，我们提出了一种新的选择特征的排名方法，该方法在多个优化模型之间达成了一致。此外，我们实现了一个众所周知的去噪算法的光谱数据在这项研究中使用。这项研究受益于2019年和2020年夏季多个实例捕获的两年遥感数据，分别有24个地块和18个地块。在美国纽约北部的两个不同地点评估了两个收获阶段模型，早期收获和晚期收获。以6个油豆角品种为材料，用产量、荚重和种子长度两个分量进行定量分析。我们使用了两种不同的植被检测算法。红边归一化差异植被指数（RENDVI）和光谱角制图仪（SAM），将字段子集划分为植被像素和非植被像素。采用偏最小二乘回归（PLSR）作为回归模型。在Jostar中嵌入的九种不同的优化模型中，我们选择了遗传算法（GA），蚁群优化（ACO），模拟退火（SA）和粒子群优化（PSO）及其联合排名。研究结果表明，荚重可以用两年数据的高决定系数（R2 = 0.78-0.93）和低均方根误差（RMSE = 940-1369 kg/ha）来解释。种子长度产量评估的精度较高（R2 = 0.83-0.98）和误差较小（RMSE = 4.245-6.018 mm）。在所使用的优化模型中，ACO和SA优于其他SAM植被检测方法相比，RENDVI方法时，密集的冠层被检查时，显示出改善的结果。在几乎所有数据集和使用的收获阶段模型中确定了450、500、520、650、700和760 nm处的波长。播后44-55天（DAP）是进行产量评估的最佳时期。未来的工作应涉及将学到的概念转移到多光谱系统，以供最终的业务使用;还应进一步关注种子长度作为地面实况数据收集技术，因为这种产量指标更加快速和直接。
Accurate, precise, and timely estimation of crop yield is key to a grower's ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400-1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78-0.93) and low root-mean-square error (RMSE = 940-1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83-0.98) and lower errors (RMSE = 4.245-6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44-55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward.