ABSTRACT The invasive Parthenium weed (Parthenium hyterophorus) adversely affects animal and human health, agricultural productivity, rural livelihoods, local and national economies, and the environment. Its fast spreading capability requires consistent monitoring for adoption of relevant mitigation approaches, potentially through remote sensing. To date, studies that have endeavoured to map the Parthenium weed have commonly used popular classification algorithms that include support vector machines and random forest classifiers, which do not capture the complex structural characteristics of the weed. Furthermore, determination of site or data specific algorithms, often achieved through intensive comparison of algorithms, is often laborious and time consuming. In addition, selected algorithms may not be optimal on datasets collected in other sites. Hence, this study adopted the Tree-based Pipeline Optimization Tool (TPOT), an automated machine learning approach that can be used to overcome high data variability during the classification process. Using Sentinel-2 and Land Satellite (Landsat) 8 imagery to map Parthenium weed, we compared the outcome of the TPOT to the best performing and optimized algorithm selected from sixteen classifiers on different training datasets. Results showed that the TPOT model yielded a higher overall classification accuracy (88.15%) using Sentinel-2 and 74% using Landsat 8, accuracies that were higher than the commonly used robust classifiers. This study is the first to demonstrate the value of TPOT in mapping Parthenium weed infestations using satellite imagery. Its adoption would therefore be useful in limiting human intervention while optimizing classification accuracies for mapping invasive plants. Based on these findings, we propose TPOT as an efficient method for selecting and tuning algorithms for Parthenium discrimination and monitoring, and indeed general vegetation mapping.

中文翻译：

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对受 Parthenium 杂草 (Parthenium hyterophorus) 侵染的热带景观进行自动分类

摘要 入侵的帕特尼姆杂草 (Parthenium hyterophorus) 对动物和人类健康、农业生产力、农村生计、地方和国家经济以及环境产生不利影响。其快速传播能力需要对相关缓解方法的采用进行持续监测，可能是通过遥感。迄今为止，努力绘制帕特尼姆杂草图的研究通常使用流行的分类算法，包括支持向量机和随机森林分类器，它们不能捕捉杂草的复杂结构特征。此外，通常通过算法的密集比较来确定站点或数据特定算法通常是费力且耗时的。此外，所选算法对于在其他站点收集的数据集可能不是最佳的。因此，本研究采用了基于树的管道优化工具 (TPOT)，这是一种自动化机器学习方法，可用于克服分类过程中的高数据可变性。使用 Sentinel-2 和 Land Satellite (Landsat) 8 图像来映射 Parthenium 杂草，我们将 TPOT 的结果与从不同训练数据集上的 16 个分类器中选择的最佳性能和优化算法进行了比较。结果表明，TPOT 模型使用 Sentinel-2 产生了更高的总体分类准确率 (88.15%)，使用 Landsat 8 产生了 74%，准确率高于常用的鲁棒分类器。这项研究首次证明了 TPOT 在使用卫星图像绘制 Parthenium 杂草侵扰地图方面的价值。因此，它的采用将有助于限制人类干预，同时优化用于绘制入侵植物图的分类精度。基于这些发现，我们提出 TPOT 作为一种有效的方法来选择和调整 Parthenium 识别和监测算法，实际上是一般的植被映射。