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Remote Sensing
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3D Point Clouds for Agriculture Applications
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3D Point Clouds for Agriculture Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 26693

Special Issue Editors

Dr. Javier Estornell

E-Mail Website
Guest Editor
GeoEnvironmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: 3D point clouds in agriculture; biomass estimation using LiDAR data; satellite images in wetlands; analysis of forest structure using LiDAR data
Dr. Edyta Hadaś

E-Mail Website
Guest Editor
Wrocław University of Environmental and Life Sciences, C.K. Norwida 25, 50-375 Wrocław, Poland
Interests: estimation of geometric parameters of fruit trees from LiDAR data; UAV for orchard inventory; point cloud processing algorithms

Special Issue Information

Dear Colleagues,

The growing availability of 3D data and the development of new technologies of Earth observation at an affordable cost, such as UAVs, allow generating new lines of research in agriculture focused on the estimation, inventory, and management of resources from fruit plantations. For some years, LiDAR technology (aerial and terrestrial systems) has been the main source in obtaining 3D point clouds. However, advances in the field of computer vision combined with the fundamentals of photogrammetry have allowed the generation of 3D point clouds from photographs taken with cameras not calibrated and installed in UAVs. From this information, structural tree parameters can be obtained automatically, on a large scale and with an acceptable precision, which can be used to predict the necessary inputs (water, fertilizers, and pesticides) and outputs (production, biomass). Field measurements of these parameters entail high personnel and time costs. It is therefore of interest to develop and adapt algorithms for the automatic determination of structural parameters from 3D point clouds. Their application in the agricultural field presents new research opportunities, with results that could be applied to improve the competitiveness of agricultural areas.

This Special Issue aims to explore the state of the art of the latest advances in the estimation of structural parameters of fruit trees from 3D point clouds and their applications in the agricultural field. This number will also cover studies that adapt existing algorithms to extract single tree widely applied to other areas as well as literature reviews. Comparisons and analysis using different measurement systems are welcome, such as: vehicle-based laser scanning (VLS); terrestrial laser scanning (TLS), airborne laser scanning (ALS), and unmanned aircraft systems (UAVS).

Dr. Javier Estornell
Dr. Edyta Hadaś
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 3D point cloud
  • Structure from Motion (SfM)
  • agricultural planning and management
  • LiDAR
  • single tree
  • biomass and carbon sequestration
  • structural parameters
  • change detection

Published Papers (6 papers)

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Research

28 pages, 2485 KiB  
Article
Making Use of 3D Models for Plant Physiognomic Analysis: A Review
by Abhipray Paturkar, Gourab Sen Gupta and Donald Bailey
Remote Sens. 2021, 13(11), 2232; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112232 - 07 Jun 2021
Cited by 20 | Viewed by 4390
Abstract
Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers. However, a review of approaches, algorithms, and techniques used for 3D plant physiognomic [...] Read more.
Use of 3D sensors in plant phenotyping has increased in the last few years. Various image acquisition, 3D representations, 3D model processing and analysis techniques exist to help the researchers. However, a review of approaches, algorithms, and techniques used for 3D plant physiognomic analysis is lacking. In this paper, we investigate the techniques and algorithms used at various stages of processing and analysing 3D models of plants, and identify their current limiting factors. This review will serve potential users as well as new researchers in this field. The focus is on exploring studies monitoring the plant growth of single plants or small scale canopies as opposed to large scale monitoring in the field. Full article
(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)
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22 pages, 8592 KiB  
Article
Canopy Parameter Estimation of Citrus grandis var. Longanyou Based on LiDAR 3D Point Clouds
by Xiangyang Liu, Yaxiong Wang, Feng Kang, Yang Yue and Yongjun Zheng
Remote Sens. 2021, 13(9), 1859; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091859 - 10 May 2021
Cited by 11 | Viewed by 3352
Abstract
The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. [...] Read more.
The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. grandis var. Longanyou were obtained to facilitate the management of groves of this species. Then, a cloth simulation filter and European clustering algorithm were used to realize individual canopy extraction. After calculating canopy height and width, canopy reconstruction and volume calculation were realized using six approaches: by a manual method and using five algorithms based on point clouds (convex hull, CH; convex hull by slices; voxel-based, VB; alpha-shape, AS; alpha-shape by slices, ASBS). ASBS is an innovative algorithm that combines AS with slices optimization, and can best approximate the actual canopy shape. Moreover, the CH algorithm had the shortest run time, and the R2 values of VCH, VVB, VAS, and VASBS algorithms were above 0.87. The volume with the highest accuracy was obtained from the ASBS algorithm, and the CH algorithm had the shortest computation time. In addition, a theoretical but preliminarily system suitable for the calculation of the canopy volume of C. grandis var. Longanyou was developed, which provides a theoretical reference for the efficient and accurate realization of future functional modules such as accurate plant protection, orchard obstacle avoidance, and biomass estimation. Full article
(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)
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17 pages, 4392 KiB  
Article
Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner
by Tyler Adams, Richard Bruton, Henry Ruiz, Ilse Barrios-Perez, Michael G. Selvaraj and Dirk B. Hays
Remote Sens. 2021, 13(7), 1272; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071272 - 26 Mar 2021
Cited by 7 | Viewed by 2782
Abstract
Challenges in rapid prototyping are a major bottleneck for plant breeders trying to develop the needed cultivars to feed a growing world population. Remote sensing techniques, particularly LiDAR, have proven useful in the quick phenotyping of many characteristics across a number of popular [...] Read more.
Challenges in rapid prototyping are a major bottleneck for plant breeders trying to develop the needed cultivars to feed a growing world population. Remote sensing techniques, particularly LiDAR, have proven useful in the quick phenotyping of many characteristics across a number of popular crops. However, these techniques have not been demonstrated with cassava, a crop of global importance as both a source of starch as well as animal fodder. In this study, we demonstrate the applicability of using terrestrial LiDAR for the determination of cassava biomass through binned height estimations, total aboveground biomass and total leaf biomass. We also tested using single LiDAR scans versus multiple registered scans for estimation, all within a field setting. Our results show that while the binned height does not appear to be an effective method of aboveground phenotyping, terrestrial laser scanners can be a reliable tool in acquiring surface biomass data in cassava. Additionally, we found that using single scans versus multiple scans provides similarly accurate correlations in most cases, which will allow for the 3D phenotyping method to be conducted even more rapidly than expected. Full article
(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)
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17 pages, 4302 KiB  
Article
Growth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry
by Marius Hobart, Michael Pflanz, Cornelia Weltzien and Michael Schirrmann
Remote Sens. 2020, 12(10), 1656; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101656 - 21 May 2020
Cited by 42 | Viewed by 4247
Abstract
In apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. [...] Read more.
In apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. The aim of this study is to delineate tree wall height information in an apple orchard applying a low-altitude flight pattern specifically designed for UAVs. This flight pattern implies small distances between the camera sensor and the tree walls when the camera is positioned in an oblique view toward the trees. In this way, it is assured that the depicted tree crown wall area will be largely covered with a larger ground sampling distance than that recorded from a nadir perspective, especially regarding the lower crown sections. Overlapping oblique view images were used to estimate 3D point cloud models by applying structure-from-motion (SfM) methods to calculate tree wall heights from them. The resulting height models were compared with ground-based light detection and ranging (LiDAR) data as reference. It was shown that the tree wall profiles from the UAV point clouds were strongly correlated with the LiDAR point clouds of two years (2018: R2 = 0.83; 2019: R2 = 0.88). However, underestimation of tree wall heights was detected with mean deviations of −0.11 m and −0.18 m for 2018 and 2019, respectively. This is attributed to the weaknesses of the UAV point clouds in resolving the very fine shoots of apple trees. Therefore, the shown approach is suitable for precise orchard management, but it underestimated vertical tree wall expanses, and widened tree gaps need to be accounted for. Full article
(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)
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21 pages, 6648 KiB  
Article
An Efficient Processing Approach for Colored Point Cloud-Based High-Throughput Seedling Phenotyping
by Si Yang, Lihua Zheng, Wanlin Gao, Bingbing Wang, Xia Hao, Jiaqi Mi and Minjuan Wang
Remote Sens. 2020, 12(10), 1540; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101540 - 12 May 2020
Cited by 19 | Viewed by 3346
Abstract
Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable [...] Read more.
Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping. Full article
(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)
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34 pages, 22300 KiB  
Article
Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models
by Mahyar Aboutalebi, Alfonso F. Torres-Rua, Mac McKee, William P. Kustas, Hector Nieto, Maria Mar Alsina, Alex White, John H. Prueger, Lynn McKee, Joseph Alfieri, Lawrence Hipps, Calvin Coopmans and Nick Dokoozlian
Remote Sens. 2020, 12(1), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010050 - 20 Dec 2019
Cited by 32 | Viewed by 6519
Abstract
In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation [...] Read more.
In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data. Full article
(This article belongs to the Special Issue 3D Point Clouds for Agriculture Applications)
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