scholarly journals Method for estimating rice plant height without ground surface detection using laser scanner measurement

2016 ◽  
Vol 10 (4) ◽  
pp. 046018 ◽  
Author(s):  
Anh Thu Thi Phan ◽  
Kazuyoshi Takahashi ◽  
Atsushi Rikimaru ◽  
Yasuhiro Higuchi
Author(s):  
A.T.T. Phan ◽  
K. Takahashi

UAV systems are considered effective tools to collect information regarding crops. In this study, the rice growth was observed by a small UAV-based LiDAR system from above. For developing the system, DJI S800 was chosen as a platform on which a non- survey-grade laser scanner HOKUYO UTM30LX-EW was mounted. Field experiments were carried out from late June to late early August 2017 in Nagaoka city, Niigata Prefecture, Japan. Percentile analysis is applied to locate the top and bottom positions of rice plants in three targeted areas. LIDAR-derived plant height is computed by taking the difference between the bottom and the rice plant's top. As a result, the LiDAR-derived canopy height well correlates to rice plant height (R2≥0.86; RMSE <6.0 cm). The small root means square error (RMSE =4.9 cm) is achieved with area 3. In the general case, the RMSE is 5.5 cm (R2=0.88). These results illustrate the capability of estimate plant height before the heading stage from UAV- based LiDAR point clouds without ground surface detection.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Liyuan Zhou ◽  
Shouye Liu ◽  
Weixun Wu ◽  
Daibo Chen ◽  
Xiaodeng Zhan ◽  
...  

2018 ◽  
Vol 6 (4) ◽  
pp. 212-234
Author(s):  
Maja Kucharczyk ◽  
Chris H. Hugenholtz ◽  
Xueyang Zou

We examined the horizontal and vertical accuracy of LiDAR data acquired from an unmanned aerial vehicle (UAV) at a field site with six vegetation types: coniferous trees, deciduous trees, short grass (0–0.3 m height), tall grass (>0.3 m height), short shrubs (0–1 m height), and tall shrubs (>1 m height). The objective was to assess positional accuracy of the ground surface in the context of digital mapping standards, and to determine how different vegetation types affect vertical accuracy. The data were acquired from a single-rotor vertical takeoff and landing UAV equipped with a Riegl VUX-1UAV laser scanner, KVH Industries 1750 IMU, and dual NovAtel GNSS receivers. Reference measurements of ground surface elevation were acquired with conventional field surveying techniques. Accuracy was evaluated using methods in the 2015 American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data. Results show that horizontal accuracy and vegetated vertical accuracy at the 95% confidence level were 0.05 and 0.24 m, respectively. Median vertical errors significantly differed among 10 of 15 vegetation type pairs, highlighting the need to account for variations of vegetation structure. According to the 2015 ASPRS standards, the reported errors fulfill the requirements for mapping at the 2 and 8 cm horizontal and vertical class levels, respectively.


2021 ◽  
Author(s):  
Chengxin Ju ◽  
Yuanyuan Zhao ◽  
Fengfeng Wu ◽  
Rui Li ◽  
Tianle Yang ◽  
...  

Abstract Background: Three-dimensional (3D) laser scanning technology could rapidly extract the surface geometric features of maize plants to achieve non-destructive monitoring of maize phenotypes. However, extracting the phenotypic parameters of maize plants based on laser point cloud data is challenging.Methods: In this paper, a rotational scanning method was used to collect the data of potted maize point cloud from different perspectives by using a laser scanner. Maize point cloud data were grid-reconstructed and aligned based on greedy projection triangulation algorithm and iterative closest point (ICP) algorithm, and the random sampling consistency algorithm was used to segment the stem and leaf point clouds of single maize plant to obtain the plant height and leaf parameters.Results: The results showed that the R2 between the predicted plant height and the measured plant height was above 0.95, and the R2 of the predicted leaf length, leaf width and leaf area were 0.938, 0878 and 0.956 respectively when compared with the measured values.Conclusions: The 3D reconstruction of maize plants using the laser scanner showed a good performance, and the phenotypic parameters obtained based on the reconstructed 3D model had high accuracy. The results were helpful to the practical application of plant 3D reconstruction and provided guidance for plant parameter acquisition and theoretical methods for intelligent agricultural research.


1994 ◽  
Vol 28 (5) ◽  
pp. 329-331 ◽  
Author(s):  
Oswaldo Paulo Forattini ◽  
Iná Kakitani ◽  
Eduardo Massad ◽  
Daniel Marucci

Studies on breeding Anopheles albitarsis and association with rice growth in irrigated paddy fields were carried out during the rice cultivation cycle from December 1993 to March 1994. This period corresponded to the length of time of permanent paddy flooding. Breeding occurred in the early stage up until five weeks after transplantation when rice plant height was small. That inverse correlation may give potential direction to control measures.


1997 ◽  
Vol 11 (2) ◽  
pp. 303-307 ◽  
Author(s):  
Sujatha Sankula ◽  
Michael P. Braverman ◽  
Steven D. Linscombe

Glufosinate at 2.2 kg ai/ha injured rice transformed with the BAR gene more when applied to one- to two-leaf (23 to 26%) than to three- to four-leaf (13 to 19%) plants. Visible injury was least when applications were made at boot stage (3 to 14%). However, applications at boot stage caused an average grain yield reduction of 16%. Most treatments did not influence rice plant height. Among single applications (0.3, 0.4, 0.6, 0.8, and 1.1 kg/ha), 1.1 kg/ha glufosinate at three- to four-leaf stage of red rice resulted in greater control (91%) than at panicle initiation (74%) or at boot stage (77%). Injury to red rice was two to 11 times greater than the injury to BAR-transformed rice depending on glufosinate rate and application timing.


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