Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.
Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.
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DOI：
10.3390/s131114662
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发表时间：
2013-10-29
期刊：
影响因子：
--
通讯作者：
Griepentrog HW
中科院分区：
文献类型：
--
作者：
Andújar D;Rueda-Ayala V;Moreno H;Rosell-Polo JR;Escolá A;Valero C;Gerhards R;Fernández-Quintanilla C;Dorado J;Griepentrog HW
在这项研究中，光探测和测距（LIDAR）传感器的精度和性能的评估植被的距离和反射测量，旨在检测和区分玉米植物和杂草从土壤表面。该研究继续了先前在西班牙玉米田中进行的工作，该工作使用了仅使用一个指数（高度分布）的激光雷达传感器。目前的制度使用上述两种指数的组合。试验在生长期12-14的玉米田中进行，在16个不同的位置选择代表三种杂草的最大可能密度：稗草（Echinochloa crus-galli（L.）P. Beauv，紫花野芝麻，猪殃殃和波斯婆婆纳。地面激光雷达传感器安装在指向行间区域的三脚架上，其水平轴和视野垂直向下指向地面，扫描可能存在植被的垂直平面。激光雷达数据采集（距离和反射测量）后，立即使用适当的方法估计植物的实际高度。为此目的，对每个取样区拍摄了数字图像。数据显示，激光雷达测量的高度和实际的植物高度之间的高度相关性（R2 = 0.75）。杂草存在/不存在与传感器读数（LIDAR高度和反射值）之间的二元逻辑回归用于验证传感器的准确性。这使得从地面植被的歧视，准确率高达95%。此外，典型判别分析（CDA）能够区分土壤和植被之间，在很小的程度上，作物和杂草之间。所研究的方法是一个很好的杂草检测系统，结合其他原理，如基于视觉的技术，可以提高除草剂喷洒的效率和准确性。
In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.
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DOI：
10.1109/tim.2007.900126
发表时间：
2007-08-01
影响因子：
5.6
作者：
Palacin, Jordi;Palleja, Tomas;Rosell, Joan Ramon
通讯作者：
Rosell, Joan Ramon
影响因子：
1.7
作者：
Gerhards, R;Christensen, S
通讯作者：
Christensen, S
影响因子：
5.1
作者：
Polo, Joan Ramon Rosell;Sanz, Ricardo;Palacin, Jordi
通讯作者：
Palacin, Jordi
影响因子：
1.7
作者：
Makowski, D.;Chauvel, B.;Munier-Jolain, N.
通讯作者：
Munier-Jolain, N.
影响因子：
3.1
作者：
Weis, Martin;Gutjahr, Christoph;Schoelderle, Florian
通讯作者：
Schoelderle, Florian