Estimating leaf area density of Osmanthus trees using ultrasonic sensing

Nan, Yulong; Zhang, Huichun; Zheng, Jiaqiang; Bian, Liming; Li, Yangxian; Yang, Yang; Zhang, Meng; Ge, Yufeng

doi:10.1016/j.biosystemseng.2019.06.020

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…where, d nm was the ultrasonic measurement distance of the canopy unit at position[n,m], (m); Dh was the distance between ultrasonic sensors, (m); v was the travel speed of the sprayer (m/s), Dt is the reciprocal of the measuring frequency of the ultrasonic sensors, (s); D 0 is the distance from the sprayer to the center of the tree row, (m). The leaf area density was estimated by the mean value of ultrasonic echo (Nan et al, 2019):…”

Section: Spray Flow Calculationmentioning

confidence: 99%

Low-volume precision spray for plant pest control using profile variable rate spraying and ultrasonic detection

et al. 2023

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Spraying chemical pesticides is one of the important means to control plant pest, and the profile variable spraying is an important technology to achieve precise pesticide application. A profiling tracking control method and an improved algorithm based on CMAC-PID (Cerebellar Model Articulation Controller- Potential Induced Degradation) were proposed in the paper. The test results of the sprayer profiling tracking of the tree canopies showed that the profiling control system using the improved algorithm had significantly better dynamic tracking performance, and the overall mean tracking error was reduced by 35.0%, compared with the traditional CMAC-PID. A spray flow calculation method based on tree canopy volume and leaf area density was proposed. Outdoor testing of the profile variable spraying and conventional spraying was carried out. There was no significant difference between the two spraying methods in terms of droplet coverage, VMD (Volume Median Diameter), NMD (Number Median Diameter), spray quality parameter and relative span coefficient, as well as droplet deposition density. The spray coefficient of variation was reduced by 25.9% and 21.9% inside and outside the tree canopy, respectively. The mean value of the ground deposition coverage of the profile variable spraying and the traditional spray was 13.0% and 33.2%, respectively, indicating a significant impact on the ground droplet deposition coverage by the two spraying methods. The spray flow rate of the profile variable spraying could be decreased by 32.1% compared to the conventional spraying. Profile variable spraying would reduce the cost associated with pesticide use and environmental pollution.

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Section: Spray Flow Calculationmentioning

confidence: 99%

Low-volume precision spray for plant pest control using profile variable rate spraying and ultrasonic detection

et al. 2023

Self Cite

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“…The time-domain energy method was used to study the canopy density by , whose work was carried out in the laboratory using artificial canopies of known density with multiple layers of leaves [17]. Nan et al (2019) built a cylindrical support with multiple layers of leaves and proposed a target leaf area density calculation method based on an ultrasonic sensor echo signal [18]. The leaf area density model of the simulation canopy was obtained, the canopy leaf area density of an Osmanthus tree was measured using the echo signals of the ultrasonic sensor, and the model feasibility was validated with the measurement.…”

Section: Introductionmentioning

confidence: 99%

Innovative Leaf Area Detection Models for Orchard Tree Thick Canopy Based on LiDAR Point Cloud Data

Zhao

Zou

et al. 2022

Agriculture

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Orchard spraying can effectively control pests and diseases. Over-spraying commonly results in excessive pesticide residues on agricultural products and environmental pollution. To avoid these problems, variable spraying technology uses target canopy detection to evaluate the leaf area in a canopy and adjust the application rate accordingly. In this study, a mobile LiDAR detection platform was set up to automatically measure point cloud data for a thick canopy in an apple orchard. A test platform was built, and manual measurements of the canopy leaf area were taken. Then, polynomial regression, back propagation (BP) neural network regression, and partial least squares regression (PLSR) algorithms were used to study the relationship between the orchard tree canopy point clouds and leaf areas. The BP neural network algorithm (86.1% and 73.6% accuracies for the test and verification data, respectively) and the PLSR algorithm (78.46% and 60.3%, respectively) performed better than the Fourier function of the polynomial regression (59.73% accuracy). The leaf area model obtained using PLSR was intuitive and simple, while the BP neural network algorithm was more accurate and could meet the requirements for high-precision variable spraying.

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“…The point cloud data collection process by 2D LiDAR is shown in Figure 1. Xu et al [27] and Nan et al [28] designed a real-time automatic target spray system based on MLS 2D LiDAR detection and compared the performance with that of infrared, ultrasonic sensors, and 3D LiDAR, proving that 2D LiDAR has great advantages in the identification accuracy and rapid measurement of distance.…”

Section: Introductionmentioning

confidence: 99%

Point Cloud Intensity Correction for 2D LiDAR Mobile Laser Scanning

et al. 2022

Wireless Communications and Mobile Computing

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The acquisition of point cloud data by mobile laser scanning (MLS) includes not only the information about the 3D geometry of the object but also the intensity from the scanned object. However, due to the influence of various factors, there is a large deviation between the intensity and the spectral reflection characteristics of the scanned object. Intensity correction should be carried out before this method is applied to object recognition. A new point cloud intensity correction method for 2D MLS that was developed by combining theoretical derivation with empirical correction is proposed in this paper. First, based on the LiDAR formula, the main factors influencing MLS intensity are investigated, and a distance piecewise polynomial and an incident angle cosine polynomial are adopted to obtain the intensity correction model of UTM-30LX 2D LiDAR on a diffuse reflector plate. Second, according to the scan pattern, a 2D scan grid is constructed to organize the MLS intensity, and a new method of spherical neighborhood search fitting plane is proposed to accurately calculate the cosine of the incident angle. Finally, the obtained intensity correction model is utilized to correct the MLS intensity of a wall. Two groups of verification experiments are carried out on single sites and multiple sites to test the effect of the intensity correction model. Overall, the improvements in intensity consistency range from 70% to 92.7% after correction within the tested ranges of distance and incident angles [0.52 m-5.34 m, 0°-74°]. The results indicate that the proposed intensity correction model yields highly accurate fitting and can effectively remove the deviation in MLS intensity caused by the distance and incident angle.

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Estimating leaf area density of Osmanthus trees using ultrasonic sensing

Cited by 19 publications

References 23 publications

Low-volume precision spray for plant pest control using profile variable rate spraying and ultrasonic detection

Low-volume precision spray for plant pest control using profile variable rate spraying and ultrasonic detection

Innovative Leaf Area Detection Models for Orchard Tree Thick Canopy Based on LiDAR Point Cloud Data

Point Cloud Intensity Correction for 2D LiDAR Mobile Laser Scanning

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