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Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery

机译：使用无人驾驶飞行器和自动图像分类在生态恢复监测中的种子和幼苗检测

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Monitoring is a crucial component of ecological recovery projects, yet it can be challengingto achieve at scale and during the formative stages of plant establishment. The monitoring of seedsand seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularlychallenging due to their diminutive size and lack of distinctive morphological characteristics. Countingand classifying seedlings to species level can be time-consuming and extremely difficult, and thereis a need for technological approaches offering restoration practitioners with fine-resolution, rapidand scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novelapproach to seed and seedling monitoring, as the combination of high-resolution sensors and lowflight altitudes allow for the detection and monitoring of small objects, even in challenging terrain andin remote areas. This study utilized low-altitude UAV imagery and an automated object-based imageanalysis software to detect and count target seeds and seedlings from a matrix of non-target grassesacross a variety of substrates reflective of local restoration substrates. Automated classification oftarget seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively,although the classification accuracy decreased with increasing flight altitude (i.e., decreasing imageresolution) and increasing background surface complexity (increasing percentage cover of non-targetgrasses and substrate surface texture). Results represent the first empirical evidence that small objectssuch as seeds and seedlings can be classified from complex ecological backgrounds using automatedprocesses from UAV-imagery with high levels of accuracy. We suggest that this novel application ofUAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliableand non-destructive early restoration trajectory assessment.
机译：监测是生态恢复项目的重要组成部分，但它可能在规模和植物建立的形成阶段实现挑战。由于它们的小小尺寸和缺乏独特的形态特征，对植物生命周期中的幼苗幼苗的监测尤其如此。将幼苗分类到物种水平的计数可能是耗时且极其困难，而且在其中需要提供具有良好分辨率的恢复从业者的技术方法，迅速扩展植物的监测解决方案。无人驾驶航空公司（无人机）为种子和幼苗监测提供了一种新颖的，因为高分辨率传感器和低空高度的组合允许检测和监测小物体，即使在挑战地形和偏远地区。本研究利用了低空UAV图像和自动对象的ImageAnalysis软件，以从非目标草段的矩阵检测和计数目标种子和幼苗各种基板反射局部恢复基板。在超过90％和80％的准确度下实现自动分类和靶幼苗，尽管随着飞行高度（即，成像凝固）的增加和增加背景表面复杂度并增加了背景表面复杂度（增加了非靶标和底物的百分比覆盖的百分比覆盖物和基材的百分比覆盖的百分比，但是分别的准确性分别分别达到了超过90％和80％的精度。表面纹理）。结果代表了第一个经验证据，即种子和幼苗可以从复杂的生态背景中分类，使用高度精度的UAV-imagery的自动化工程分类。我们建议，这种新型应用在生态监测中的应用提供了恢复从业者的快速，ReliaBleand无损早期恢复轨迹评估。

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