Department of Animal & Plant Science, University of Sheffield, Sheffield, U.K.
Pest Manag Sci. 2019 Aug;75(8):2283-2294. doi: 10.1002/ps.5444. Epub 2019 May 21.
It is important to map agricultural weed populations to improve management and maintain future food security. Advances in data collection and statistical methodology have created new opportunities to aid in the mapping of weed populations. We set out to apply these new methodologies (unmanned aerial systems; UAS) and statistical techniques (convolutional neural networks; CNN) to the mapping of black-grass, a highly impactful weed in wheat fields in the UK. We tested this by undertaking extensive UAS and field-based mapping over the course of 2 years, in total collecting multispectral image data from 102 fields, with 76 providing informative data. We used these data to construct a vegetation index (VI), which we used to train a custom CNN model from scratch. We undertook a suite of data engineering techniques, such as balancing and cleaning to optimize performance of our metrics. We also investigate the transferability of the models from one field to another.
The results show that our data collection methodology and implementation of CNN outperform pervious approaches in the literature. We show that data engineering to account for 'artefacts' in the image data increases our metrics significantly. We are not able to identify any traits that are shared between fields that result in high scores from our novel leave one field our cross validation (LOFO-CV) tests.
We conclude that this evaluation procedure is a better estimation of real-world predictive value when compared with past studies. We conclude that by engineering the image data set into discrete classes of data quality we increase the prediction accuracy from the baseline model by 5% to an area under the curve (AUC) of 0.825. We find that the temporal effects studied here have no effect on our ability to model weed densities. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
对农业杂草种群进行测绘对于改善管理和维护未来粮食安全至关重要。数据收集和统计方法的进步为辅助杂草种群测绘创造了新的机会。我们着手应用这些新方法（无人机系统；UAS）和统计技术（卷积神经网络；CNN）来测绘英国麦田中极具影响力的黑麦草。为此，我们在两年的时间里进行了广泛的 UAS 和实地测绘，总共从 102 个地块收集了多光谱图像数据，其中 76 个提供了有用数据。我们使用这些数据构建了一个植被指数（VI），并使用该指数从头开始训练一个自定义 CNN 模型。我们采用了一系列数据工程技术，例如平衡和清理，以优化我们指标的性能。我们还研究了模型从一个地块到另一个地块的可转移性。
结果表明，我们的数据收集方法和 CNN 的实施优于文献中的先前方法。我们表明，考虑到图像数据中的“伪影”进行数据工程可以显著提高我们的指标。我们无法识别出任何在我们的新留一地块交叉验证（LOFO-CV）测试中导致高分数的地块之间共享的特征。
我们的结论是，与过去的研究相比，这种评估程序是对现实世界预测值的更好估计。我们的结论是，通过将图像数据集工程成离散的数据质量类，我们将基线模型的预测精度提高了 5%，达到 0.825 的曲线下面积（AUC）。我们发现，这里研究的时间效应对我们建模杂草密度的能力没有影响。©2019 作者。Pest Management Science 由 John Wiley & Sons Ltd 代表化学工业协会出版。
