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Journal articles on the topic 'Canopy volume detection'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Wang, Mengmeng, Hanjie Dou, Hongyan Sun, Changyuan Zhai, Yanlong Zhang, and Feixiang Yuan. "Calculation Method of Canopy Dynamic Meshing Division Volumes for Precision Pesticide Application in Orchards Based on LiDAR." Agronomy 13, no. 4 (2023): 1077. http://dx.doi.org/10.3390/agronomy13041077.

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The canopy volume of fruit trees is an important input for the precise and varying application of pesticides in orchards. The fixed mesh division method is mostly used to calculate canopy volumes with variable target-oriented spraying. To reduce the influence of the working speed on the detection accuracy under a fixed mesh width division, the cuboid accumulation of divided areas (CADAs), which is a light detection and ranging (LiDAR) online detection method for a fruit tree canopy volume based on dynamic mesh division, is proposed in this paper. In the method, the area is divided according to

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Gu, Chenchen, Xiu Wang, Xiaole Wang, Fuzeng Yang, and Changyuan Zhai. "Research Progress on Variable-Rate Spraying Technology in Orchards." Applied Engineering in Agriculture 36, no. 6 (2020): 927–42. http://dx.doi.org/10.13031/aea.14201.

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HighlightsReview the research status of variable-rate spraying technology and point out the direction for the future researchDiscussed the advantages and disadvantages of different techniques to detect canopy volume and canopy biomassThe air speed and volume adjustment need to be controlled based on the canopy detection systemAbstract. Variable-rate pesticide application in orchards aims to solve the problems of low pesticide utilization rates and serious environmental pollution in traditional pesticide applications. In this article, we have reviewed the research status of the technology to po

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Gang, Min-Seok, Thanyachanok Sutthanonkul, Won Suk Lee, Shiyu Liu, and Hak-Jin Kim. "Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network." Sensors 24, no. 21 (2024): 6920. http://dx.doi.org/10.3390/s24216920.

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Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual mane

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Roman, Carla, Hongyoung Jeon, Heping Zhu, Javier Campos, and Erdal Ozkan. "Stereo Vision Controlled Variable Rate Sprayer for Specialty Crops: Part II. Sprayer Development and Performance Evaluation." Journal of the ASABE 66, no. 5 (2023): 1005–17. http://dx.doi.org/10.13031/ja.15578.

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Highlights A real time stereo vision controlled variable rate sprayer for specialty crops was developed. The stereo vision system of the sprayer detected outdoor trees with similar canopy profiles under travel speeds ranging from 3.2 to 8 km h-1. Canopy volume measurements of the sprayer were impacted by lateral distances between the sprayer and the tree center and travel speeds. The sprayer required less than 200 ms from tree canopy detection to spray decisions. The sprayer achieved spray volume reductions from 72.6% to 80.5% compared to constant rate spray application. Abstract. A real time

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Gu, Chenchen, Changyuan Zhai, Xiu Wang, and Songlin Wang. "CMPC: An Innovative Lidar-Based Method to Estimate Tree Canopy Meshing-Profile Volumes for Orchard Target-Oriented Spray." Sensors 21, no. 12 (2021): 4252. http://dx.doi.org/10.3390/s21124252.

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Canopy characterization detection is essential for target-oriented spray, which minimizes pesticide residues in fruits, pesticide wastage, and pollution. In this study, a novel canopy meshing-profile characterization (CMPC) method based on light detection and ranging (LiDAR)point-cloud data was designed for high-precision canopy volume calculations. First, the accuracy and viability of this method were tested using a simulated canopy. The results show that the CMPC method can accurately characterize the 3D profiles of the simulated canopy. These simulated canopy profiles were similar to those

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Zhou, Huitao, Weidong Jia, Yong Li, and Mingxiong Ou. "Method for Estimating Canopy Thickness Using Ultrasonic Sensor Technology." Agriculture 11, no. 10 (2021): 1011. http://dx.doi.org/10.3390/agriculture11101011.

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The accurate detection of canopy characteristics is the basis of precise variable spraying. Canopy characteristics such as canopy density, thickness and volume are needed to vary the pesticide application rate and adjust the spray flow rate and air supply volume. Canopy thickness is an important canopy dimension for the calculation of tree canopy volume in pesticide variable spraying. With regard to the phenomenon of ultrasonic waves with multiple reflections and the further analysis of echo signals, we found that there is a proportional relationship between the canopy thickness and echo inter

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Saha, Kowshik Kumar, Nikos Tsoulias, Cornelia Weltzien, and Manuela Zude-Sasse. "Estimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner." Horticulturae 8, no. 2 (2022): 90. http://dx.doi.org/10.3390/horticulturae8020090.

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Monitoring of plant vegetative growth can provide the basis for precise crop management. In this study, a 2D light detection and ranging (LiDAR) laser scanner, mounted on a linear conveyor, was used to acquire multi-temporal three-dimensional (3D) data from strawberry plants (‘Honeoye’ and ‘Malling Centenary’) 14–77 days after planting (DAP). Canopy geometrical variables, i.e., points per plant, height, ground projected area, and canopy volume profile, were extracted from 3D point cloud. The manually measured leaf area exhibited a linear relationship with LiDAR-derived parameters (R2 = 0.98, 0

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Lim, Kevin, Paul Treitz, Michael Wulder, Benoît St-Onge, and Martin Flood. "LiDAR remote sensing of forest structure." Progress in Physical Geography: Earth and Environment 27, no. 1 (2003): 88–106. http://dx.doi.org/10.1191/0309133303pp360ra.

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Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

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Jiang, Yinlong, Jieli Duan, Yang Li, Jiaxiang Yu, Zhou Yang, and Xing Xu. "Fruit Orchard Canopy Recognition and Extraction of Characteristics Based on Millimeter-Wave Radar." Agriculture 15, no. 13 (2025): 1342. https://doi.org/10.3390/agriculture15131342.

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Fruit orchard canopy recognition and characteristic extraction are the key problems faced in orchard precision production. To this end, we built a fruit tree canopy detection platform based on millimeter-wave radar, verified the feasibility of millimeter-wave radar from the two perspectives of fruit orchard canopy recognition and canopy characteristic extraction, and explored the detection accuracy of millimeter-wave radar under spray conditions. For fruit orchard canopy recognition, based on the DBSCAN algorithm, an ellipsoid model adaptive clustering algorithm based on a variable-axis (E-DBS

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Colaço, A. F., R. G. Trevisan, J. P. Molin, J. R. Rosell-Polo, and A. Escolà. "Orange tree canopy volume estimation by manual and LiDAR-based methods." Advances in Animal Biosciences 8, no. 2 (2017): 477–80. http://dx.doi.org/10.1017/s2040470017001133.

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LiDAR (Light detection and ranging) technology is an alternative to current manual methods of canopy geometry estimations in orange trees. The objective of this work was to compare different types of canopy volume estimations of orange trees, some inspired on manual methods and others based on a LiDAR sensor. A point cloud was generated for 25 individual trees using a laser scanning system. The convex-hull and the alpha-shape surface reconstruction algorithms were tested. LiDAR derived models are able to represent orange trees more accurately than traditional methods. However, results differ s

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Perna, Carolina, Andrea Pagliai, Daniele Sarri, Riccardo Lisci, and Marco Vieri. "Can a Light Detection and Ranging (LiDAR) and Multispectral Sensor Discriminate Canopy Structure Changes Due to Pruning in Olive Growing? A Field Experimentation." Sensors 24, no. 24 (2024): 7894. https://doi.org/10.3390/s24247894.

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The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees’ canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response. The 2D LiDAR was used to assess its ability to discriminate volume before and after pruning. Data were collected in a traditional olive grove loc

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Gu, Chenchen, Jiahui Sun, Si Li, Shuo Yang, Wei Zou, and Changyuan Zhai. "Deposition Characteristics of Air-Assisted Sprayer Based on Canopy Volume and Leaf Area of Orchard Trees." Plants 14, no. 2 (2025): 220. https://doi.org/10.3390/plants14020220.

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Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies. The spray experiments were conducted on canopies of different regions, using

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Hermosilla, Txomin, Luis A. Ruiz, Alexandra N. Kazakova, Nicholas C. Coops, and L. Monika Moskal. "Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data." International Journal of Wildland Fire 23, no. 2 (2014): 224. http://dx.doi.org/10.1071/wf13086.

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Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, i

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Leite, Rodrigo Vieira, Cibele Hummel do Amaral, Raul de Paula Pires, et al. "Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches." Remote Sensing 12, no. 9 (2020): 1513. http://dx.doi.org/10.3390/rs12091513.

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Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collecte

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Thorat, Deepak, C. R. Mehta, K. N. Agrawal, Bikram Jyoti, Manoj Kumar, and N. S. Chandel. "Performance Evaluation of Variable Rate Spraying System under Simulated Conditions." Journal of Agricultural Engineering (India) 61, no. 2 (2024): 133–46. http://dx.doi.org/10.52151/jae2024612.1839.

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Precise canopy measurement is the key to success for a variable rate spraying system. In this study, a variable rate spraying system was developed and its performance was evaluated under simulated laboratory conditions. The calibration and testing of individual components of the system were performed before integration into a system. Linear relationship was observed between distance and voltage (R2= 0.995) during calibration of the ultrasonic sensor. The detection range of ultrasonic sensor in real working conditions was found in the range of 0.20 to 7.5 m. It was maximum with flat surface boa

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Kumbhar, Avadhut Shankar Salavi, and Prof Mrs S. S. Patil. "Orchard Mapping with Deep Learning Semantic Segmentation." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 174–76. http://dx.doi.org/10.22214/ijraset.2023.56465.

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Abstract: The goal of orchard mapping with deep learning semantic segmentation is to automatic detection and localization of the orchard tree canopy under varied situations like multiple seasons, various tree ages, and varying levels of weed covering. The accuracy of segmentation depends on many factors like season, canopy size, and presence of weeds, background soil condition, and untreated soil. The proposed research have several combinations of training & test data based on orchard conditions The proposed research work contains deep learning convolutional neural network variant. The alg

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Zhang, Wenli, Xinyu Peng, Tingting Bai, Haozhou Wang, Daisuke Takata, and Wei Guo. "A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees." Remote Sensing 16, no. 9 (2024): 1570. http://dx.doi.org/10.3390/rs16091570.

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This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techn

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Römer, Christoph, Mirwaes Wahabzada, Agim Ballvora, et al. "Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis." Functional Plant Biology 39, no. 11 (2012): 878. http://dx.doi.org/10.1071/fp12060.

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Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis. Classical supervised learning algorithms often fail in applied plant sciences due to their need of labelled datasets, which are difficult to obtain. Therefore, new approaches for unsupervised learning of relevant patterns are needed. We apply for the first time a recent matrix factorisati

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APOSTOL, Bogdan, Adrian LORENT, Marius PETRILA, Vladimir GANCZ, and Ovidiu BADEA. "Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [Picea abies (L.) Karst.] Test Site in Romania." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 44, no. 1 (2016): 313–23. http://dx.doi.org/10.15835/nbha44110155.

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The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a

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Parr, Baden, Mathew Legg, Stuart Bradley, and Fakhrul Alam. "Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array." Sensors 21, no. 6 (2021): 2182. http://dx.doi.org/10.3390/s21062182.

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Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directiona

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Luo, Taige, Shuyu Rao, Wenjun Ma, et al. "YOLOTree-Individual Tree Spatial Positioning and Crown Volume Calculation Using UAV-RGB Imagery and LiDAR Data." Forests 15, no. 8 (2024): 1375. http://dx.doi.org/10.3390/f15081375.

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Individual tree canopy extraction plays an important role in downstream studies such as plant phenotyping, panoptic segmentation and growth monitoring. Canopy volume calculation is an essential part of these studies. However, existing volume calculation methods based on LiDAR or based on UAV-RGB imagery cannot balance accuracy and real-time performance. Thus, we propose a two-step individual tree volumetric modeling method: first, we use RGB remote sensing images to obtain the crown volume information, and then we use spatially aligned point cloud data to obtain the height information to autom

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Liu, Chong, and Zhen Feng Shao. "Estimation of Forest Carbon Storage Based on Airborne LiDAR Data." Applied Mechanics and Materials 195-196 (August 2012): 1314–20. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1314.

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The objective of this study was to estimate the carbon storage of forest areas by using airborne light detection and ranging (LiDAR) data. Digital canopy height model (CHM) which generated from point cloud data was combined with accurate geo-referenced true color orthophoto image to produce parameter information of trees (height, canopy diameter, DBH data) in test area by marker controlled watershed segmentation algorithm. The total carbon storage of experimental site could be calculated by adopting binary tree volume table and semi-empirical inversion means. This study suggested that the carb

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Hyyppä, Eric, Xiaowei Yu, Harri Kaartinen, et al. "Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests." Remote Sensing 12, no. 20 (2020): 3327. http://dx.doi.org/10.3390/rs12203327.

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In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height an

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Alvites, Cesar, Hannah O’Sullivan, Saverio Francini, et al. "High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data." Remote Sensing 16, no. 7 (2024): 1281. http://dx.doi.org/10.3390/rs16071281.

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Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispers

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Lopes Queiroz, Gustavo, Gregory McDermid, Julia Linke, Christopher Hopkinson, and Jahan Kariyeva. "Estimating Coarse Woody Debris Volume Using Image Analysis and Multispectral LiDAR." Forests 11, no. 2 (2020): 141. http://dx.doi.org/10.3390/f11020141.

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Coarse woody debris (CWD, parts of dead trees) is an important factor in forest management, given its roles in promoting local biodiversity and unique microhabitats, as well as providing carbon storage and fire fuel. However, parties interested in monitoring CWD abundance lack accurate methods to measure CWD accurately and extensively. Here, we demonstrate a novel strategy for mapping CWD volume (m3) across a 4300-hectare study area in the boreal forest of Alberta, Canada using optical imagery and an infra-canopy vegetation-index layer derived from multispectral aerial LiDAR. Our models predic

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Blackman, Raoul, and Fei Yuan. "Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data." Remote Sensing 12, no. 11 (2020): 1820. http://dx.doi.org/10.3390/rs12111820.

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Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it pos

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Zhang, Jinhui, Yunfu Chen, Chao Gu, et al. "A variable-rate spraying method fusing canopy volume and disease detection to reduce pesticide dosage." Computers and Electronics in Agriculture 237 (October 2025): 110606. https://doi.org/10.1016/j.compag.2025.110606.

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Raman, Mugilan Govindasamy, Eduardo Fermino Carlos, and Sindhuja Sankaran. "Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees." Sensors 22, no. 12 (2022): 4619. http://dx.doi.org/10.3390/s22124619.

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Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of

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Sun, Wang, Ding, Lu, and Sun. "Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry." Agronomy 9, no. 11 (2019): 774. http://dx.doi.org/10.3390/agronomy9110774.

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Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the

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Sangjan, Worasit, and Sindhuja Sankaran. "Phenotyping Architecture Traits of Tree Species Using Remote Sensing Techniques." Transactions of the ASABE 64, no. 5 (2021): 1611–24. http://dx.doi.org/10.13031/trans.14419.

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HighlightsTree canopy architecture traits are associated with its productivity and management.Understanding these traits is important for both precision agriculture and phenomics applications.Remote sensing platforms (satellite, UAV, etc.) and multiple approaches (SfM, LiDAR) have been used to assess these traits.3D reconstruction of tree canopies allows the measurement of tree height, crown area, and canopy volume.Abstract. Tree canopy architecture is associated with light use efficiency and thus productivity. Given the modern training systems in orchard tree fruit systems, modification of tr

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Pirotti, F., C. Paterno, and M. Pividori. "APPLICATION OF TREE DETECTION METHODS OVER LIDAR DATA FOR FOREST VOLUME ESTIMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1055–60. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1055-2020.

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Abstract. Lidar (light detection and ranging) data are becoming more and more important in the analysis of the most relevant forest parameters. This study aims to compare the most recent segmentation methods for single trees using the ALS (Airborne Laser Scanning) point cloud and the CHM (Canopy Height Model). The methods used were the Li et al., method developed in 2012 and the Multi CHM method developed in 2015. The parameters analysed were the height and diameter for the individual trees and the volume and density for the entire forest. The efficiency of each method was verified by comparin

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Di Gennaro, Salvatore Filippo, Carla Nati, Riccardo Dainelli, et al. "An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards." Forests 11, no. 3 (2020): 308. http://dx.doi.org/10.3390/f11030308.

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The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool fo

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Muhojoki, Jesse, Daniella Tavi, Eric Hyyppä, et al. "Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions." Remote Sensing 16, no. 10 (2024): 1721. http://dx.doi.org/10.3390/rs16101721.

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The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the

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Yan, Tingting, Heping Zhu, Li Sun, Xiaochan Wang, and Peter Ling. "Investigation of an Experimental Laser Sensor-Guided Spray Control System for Greenhouse Variable-Rate Applications." Transactions of the ASABE 62, no. 4 (2019): 899–911. http://dx.doi.org/10.13031/trans.13366.

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Abstract. Precision variable-rate spraying technology is needed for controlled-environment plant production in greenhouses. An experimental spray system for greenhouse applications was developed for real-time control of individual nozzle outputs. The system mainly consisted of a high-speed laser scanning sensor, 12 individual variable-rate nozzles, an embedded computer, a spray control unit, and a 3.6 m long mobile spray boom. Each nozzle was coupled with a pulse-width modulated solenoid valve to discharge sprays at variable rates based on target presence and plant canopy structure. Laboratory

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Cantón-Martínez, Susana, Francisco Javier Mesas-Carrascosa, Raúl de la Rosa, et al. "Evaluation of Canopy Growth in Rainfed Olive Hedgerows Using UAV-LiDAR." Horticulturae 10, no. 9 (2024): 952. http://dx.doi.org/10.3390/horticulturae10090952.

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Hedgerow cultivation systems have revolutionized olive growing in recent years because of the mechanization of harvesting. Initially applied under irrigated conditions, its use has now extended to rainfed cultivation. However, there is limited information on the behavior of olive cultivars in hedgerow growing systems under rainfed conditions, which is a crucial issue in the context of climate change. To fill this knowledge gap, a rainfed cultivar trial was planted in 2020 in Southern Spain to compare ‘Arbequina’, ‘Arbosana’, ‘Koroneiki’, and ‘Sikitita’, under such growing conditions. One of th

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O'Keefe, Joy M., Susan C. Loeb, Hoke S. Hill, and Lanham J. Drew. "Quantifying clutter: A comparison of four methods and their relationship to bat detection." Forest Ecology and Management 322 (June 12, 2014): 1–9. https://doi.org/10.5281/zenodo.13442801.

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(Uploaded by Plazi for the Bat Literature Project) The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these to the probability of detecting bat calls. From June to July, 2005–2006, we used Anabat detectors to conduct acoustic surveys for 2–4 nights at each of 71 points representing thr

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O'Keefe, Joy M., Susan C. Loeb, Hoke S. Hill, and Lanham J. Drew. "Quantifying clutter: A comparison of four methods and their relationship to bat detection." Forest Ecology and Management 322 (June 7, 2014): 1–9. https://doi.org/10.5281/zenodo.13442801.

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(Uploaded by Plazi for the Bat Literature Project) The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these to the probability of detecting bat calls. From June to July, 2005–2006, we used Anabat detectors to conduct acoustic surveys for 2–4 nights at each of 71 points representing thr

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O'Keefe, Joy M., Susan C. Loeb, Hoke S. Hill, and Lanham J. Drew. "Quantifying clutter: A comparison of four methods and their relationship to bat detection." Forest Ecology and Management 322 (July 3, 2014): 1–9. https://doi.org/10.5281/zenodo.13442801.

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(Uploaded by Plazi for the Bat Literature Project) The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these to the probability of detecting bat calls. From June to July, 2005–2006, we used Anabat detectors to conduct acoustic surveys for 2–4 nights at each of 71 points representing thr

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O'Keefe, Joy M., Susan C. Loeb, Hoke S. Hill, and Lanham J. Drew. "Quantifying clutter: A comparison of four methods and their relationship to bat detection." Forest Ecology and Management 322 (July 10, 2014): 1–9. https://doi.org/10.5281/zenodo.13442801.

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Abstract:

(Uploaded by Plazi for the Bat Literature Project) The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these to the probability of detecting bat calls. From June to July, 2005–2006, we used Anabat detectors to conduct acoustic surveys for 2–4 nights at each of 71 points representing thr

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O'Keefe, Joy M., Susan C. Loeb, Hoke S. Hill, and Lanham J. Drew. "Quantifying clutter: A comparison of four methods and their relationship to bat detection." Forest Ecology and Management 322 (July 17, 2014): 1–9. https://doi.org/10.5281/zenodo.13442801.

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Abstract:

(Uploaded by Plazi for the Bat Literature Project) The degree of spatial complexity in the environment, or clutter, affects the quality of foraging habitats for bats and their detection with acoustic systems. Clutter has been assessed in a variety of ways but there are no standardized methods for measuring clutter. We compared four methods (Visual Clutter, Cluster, Single Variable, and Clutter Index) and related these to the probability of detecting bat calls. From June to July, 2005–2006, we used Anabat detectors to conduct acoustic surveys for 2–4 nights at each of 71 points representing thr

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Luo, Yu, Xiaoli He, Hanwen Shi, Simon X. Yang, Lepeng Song, and Ping Li. "Design and Development of a Precision Spraying Control System for Orchards Based on Machine Vision Detection." Sensors 25, no. 12 (2025): 3799. https://doi.org/10.3390/s25123799.

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Precision spraying technology has attracted increasing attention in orchard production management. Traditional chemical pesticide application relies on subjective judgment, leading to fluctuations in pesticide usage, low application efficiency, and environmental pollution. This study proposes a machine vision-based precision spraying control system for orchards. First, a canopy leaf wall area calculation method was developed based on a multi-iteration GrabCut image segmentation algorithm, and a spray volume calculation model was established. Next, a fuzzy adaptive control algorithm based on an

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Hollaus, M., W. Wagner, K. Schadauer, B. Maier, and K. Gabler. "Growing stock estimation for alpine forests in Austria: a robust lidar-based approach." Canadian Journal of Forest Research 39, no. 7 (2009): 1387–400. http://dx.doi.org/10.1139/x09-042.

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The overall goal of this study was to describe a novel area-based semiempirical model for estimating growing stock from small-footprint light detection and ranging (lidar) data. The model assumes a linear relationship between growing stock and lidar-derived canopy volume that is stratified according to several canopy height classes to account for height dependent differences in canopy structure and nonlinear tree size-shape relationships. It was applied over a 128 km2 alpine area in Austria where operational forest inventory data and lidar data acquired in winter and summer were available. The

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Gao, Sha, Zhengnan Zhang, and Lin Cao. "Individual Tree Structural Parameter Extraction and Volume Table Creation Based on Near-Field LiDAR Data: A Case Study in a Subtropical Planted Forest." Sensors 21, no. 23 (2021): 8162. http://dx.doi.org/10.3390/s21238162.

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Individual tree structural parameters are vital for precision silviculture in planted forests. This study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit volume models in subtropical planted forests in southeastern China. To do this, firstly, the tree height was acquired from ULS data and the diameter at breast height (DBH) was acquired from BLS data by using individual tree segmentation algorithms. Secondly, point clouds of the complet

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Mikita, Tomáš, and Petr Balogh. "Usage of Geoprocessing Services in Precision Forestry for Wood Volume Calculation and Wind Risk Assessment." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 3 (2015): 793–801. http://dx.doi.org/10.11118/actaun201563030793.

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This paper outlines the idea of a precision forestry tool for optimizing clearcut size and shape within the process of forest recovery and its publishing in the form of a web processing service for forest owners on the Internet. The designed tool titled COWRAS (Clearcut Optimization and Wind Risk Assessment) is developed for optimization of clearcuts (their location, shape, size, and orientation) with subsequent wind risk assessment. The tool primarily works with airborne LiDAR data previously processed to the form of a digital surface model (DSM) and a digital elevation model (DEM). In the fi

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Kuo, Kuangting, Kenta Itakura, and Fumiki Hosoi. "Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR." Remote Sensing 11, no. 21 (2019): 2536. http://dx.doi.org/10.3390/rs11212536.

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It is critical to take the variability of leaf angle distribution into account in a remote sensing analysis of a canopy system. Due to the physical limitations of field measurements, it is difficult to obtain leaf angles quickly and accurately, especially with a complicated canopy structure. An application of terrestrial LiDAR (Light Detection and Ranging) is a common solution for the purposes of leaf angle estimation, and it allows for the measurement and reconstruction of 3D canopy models with an arbitrary volume of leaves. However, in most cases, the leaf angle is estimated incorrectly due

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Afsar, Muhammad Munir, Asim Dilawar Bakhshi, Muhammad Shahid Iqbal, Ejaz Hussain, and Javed Iqbal. "High-Precision Mango Orchard Mapping Using a Deep Learning Pipeline Leveraging Object Detection and Segmentation." Remote Sensing 16, no. 17 (2024): 3207. http://dx.doi.org/10.3390/rs16173207.

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Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Paki

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Dou, Hanjie, Changyuan Zhai, Liping Chen, Xiu Wang, and Wei Zou. "Comparison of Orchard Target-Oriented Spraying Systems Using Photoelectric or Ultrasonic Sensors." Agriculture 11, no. 8 (2021): 753. http://dx.doi.org/10.3390/agriculture11080753.

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Orchard pesticide off-target deposition and drift cause substantial soil and water pollution, and other environmental pollution. Orchard target-oriented spraying technologies have been used to reduce the deposition and drift caused by off-target spraying and control environmental pollution to within an acceptable range. Two target-oriented spraying systems based on photoelectric sensors or ultrasonic sensors were developed. Three spraying treatments of young cherry trees and adult apple trees were conducted using a commercial sprayer with a photoelectric-based target-oriented spraying system,

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Tsoulias, Nikos, Dimitrios S. Paraforos, Spyros Fountas, and Manuela Zude-Sasse. "Estimating Canopy Parameters Based on the Stem Position in Apple Trees Using a 2D LiDAR." Agronomy 9, no. 11 (2019): 740. http://dx.doi.org/10.3390/agronomy9110740.

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Data of canopy morphology are crucial for cultivation tasks within orchards. In this study, a 2D light detection and range (LiDAR) laser scanner system was mounted on a tractor, tested on a box with known dimensions (1.81 m × 0.6 m × 0.6 m), and applied in an apple orchard to obtain the 3D structural parameters of the trees (n = 224). The analysis of a metal box which considered the height of four sides resulted in a mean absolute error (MAE) of 8.18 mm with a bias (MBE) of 2.75 mm, representing a root mean square error (RMSE) of 1.63% due to gaps in the point cloud and increased incident angl

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Parker, Robert C., and David L. Evans. "LiDAR Forest Inventory with Single-Tree, Double-, and Single-Phase Procedures." International Journal of Forestry Research 2009 (2009): 1–6. http://dx.doi.org/10.1155/2009/864108.

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Light Detection and Ranging (LiDAR) data at 0.5–2 m postings were used with double-sample, stratified procedures involving single-tree relationships in mixed, and single species stands to yield sampling errors ranging from % to %. LiDAR samples were selected with focal filter procedures and heights computed from interpolated canopy and DEM surfaces. Tree dbh and height data were obtained at various ratios of LiDAR, ground samples for DGPS located ground plots. Dbh-height and ground-LiDAR height models were used to predict dbh and compute Phase 2 estimates of basal area and volume. Phase 1 esti

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Chiappini, Stefano, Mattia Balestra, Federico Giulioni, Ernesto Marcheggiani, Eva Savina Malinverni, and Roberto Pierdicca. "Comparing the accuracy of 3D urban olive tree models detected by smartphone using LiDAR sensor, photogrammetry and NeRF: a case study of ’Ascolana Tenera’ in Italy." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-3-2024 (November 4, 2024): 61–68. http://dx.doi.org/10.5194/isprs-annals-x-3-2024-61-2024.

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Abstract. Rapid urban growth makes green space management crucial to improve citizens’ well-being. Urban olive trees characterize the Italian landscapes and their culture. This study explores different methodologies for urban tree assessment in this context, using an iPhone 14 Pro Max. These included: 1) its integrated Light Detection and Ranging (LiDAR) sensor using the Recon3D app, 2) its camera with Structure from Motion (SfM) techniques, and 3) its camera for generating 3D models using Neural Radiance Fields (NeRF). Additionally, a professional Mobile Laser Scanner (MLS), was used for comp

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